
# Import Libraries
import requests
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import re


# Code for the collection of the data
# here list of 4 cities are taken and collection of data is done for them
cities = ['bangalore','mumbai','chennai','visakhapatnam']
price=[]
per_sqt=[]
BHK=[]
Carpet_Area=[]
Owner=[]
Floor=[]
Area=[]
City=[]
for j in cities:
    url = "https://www.magicbricks.com/flats-in-"+j+"-for-sale-pppfs"
    print(url)
    page = requests.get(url)
    soup = BeautifulSoup(page.text)
    
    for i in soup.find_all("div",class_="mb-srp__card__price--amount"):
        price.append(i.text)
        
    for i in soup.find_all("div",class_="mb-srp__card__price--size"):
        per_sqt.append(i.text.split(' ')[0])
        
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        BHK.append(i.text[0])
        
    for i in soup.find_all("div",class_="mb-srp__card__summary--value"):
        a =i.text
        b = re.findall(r"(\d+)\ssqft",str(a))
        if b:
            Carpet_Area.append(b[0])
            
    for i in soup.find_all('div',class_='mb-srp__card__ads--name'):
        Owner.append(i.text.split(':')[1])
            
    for i in soup.find_all("div",class_="mb-srp__card__summary__list--item"):
        a = i.text
        b = re.findall(r"\w+\s\w+\s\w+\s\d+",a)
        if b:
            Floor.append(b[0])
            
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        Area.append(i.text.split('in')[1].split(',')[0])
        City.append(j)
    print(i,'---->', len(Owner))
    print(i,'---->', len(Floor))
    print(i,'---->', len(per_sqt))

https://www.magicbricks.com/flats-in-bangalore-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yelahanka , Bangalore</h2> ----> 30
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yelahanka , Bangalore</h2> ----> 30
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yelahanka , Bangalore</h2> ----> 29
https://www.magicbricks.com/flats-in-mumbai-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Mulund West , Mumbai</h2> ----> 60
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Mulund West , Mumbai</h2> ----> 58
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Mulund West , Mumbai</h2> ----> 52
https://www.magicbricks.com/flats-in-chennai-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Chennai</h2> ----> 90
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Chennai</h2> ----> 88
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Chennai</h2> ----> 81
https://www.magicbricks.com/flats-in-visakhapatnam-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in MVP Colony , Visakhapatnam</h2> ----> 120
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in MVP Colony , Visakhapatnam</h2> ----> 118
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in MVP Colony , Visakhapatnam</h2> ----> 110


# printing to see the values are obtained are not
print(price)
print(per_sqt)
print(BHK)
print(Carpet_Area)
print(Owner)
print(Floor)
print(Area)
print(City)

# Finding the length to see is all the lengths are same
print(len(price))
print(len(per_sqt))
print(len(BHK))
print(len(Carpet_Area))
print(len(Owner))
print(len(Floor))
print(len(Area))
print(len(City))

['₹60 Lac ', '₹80 Lac ', '₹48 Lac ', '₹1.35 Cr ', '₹61 Lac ', '₹1.30 Cr ', '₹50 Lac ', '₹1.80 Cr ', '₹1 Cr ', '₹1.02 Cr ', '₹1.45 Cr ', '₹3.85 Cr ', '₹70 Lac ', '₹85 Lac ', '₹98 Lac ', '₹60 Lac ', '₹78 Lac ', '₹57 Lac ', '₹1.08 Cr ', '₹57 Lac ', '₹35 Lac ', '₹1.80 Cr ', '₹2.75 Cr ', '₹1.12 Cr ', '₹1 Cr ', '₹1.45 Cr ', '₹80 Lac ', '₹1.35 Cr ', '₹2.10 Cr ', '₹51.2 Lac ', '₹73 Lac ', '₹22 Lac ', '₹2.25 Cr ', '₹2.70 Cr ', '₹41 Lac ', '₹5.99 Cr ', '₹1.20 Cr ', '₹1.30 Cr ', '₹65 Lac ', '₹3.75 Cr ', '₹2.70 Cr ', '₹1.02 Cr ', '₹95 Lac ', '₹60 Lac ', '₹15 Cr ', '₹32 Lac ', '₹98 Lac ', '₹4.89 Cr ', '₹44.9 Lac ', '₹70 Lac ', '₹1.05 Cr ', '₹30 Lac ', '₹1.25 Cr ', '₹1.90 Cr ', '₹1.55 Cr ', '₹1.28 Cr ', '₹2 Cr ', '₹2.52 Cr ', '₹1 Cr ', '₹1.60 Cr ', '₹55 Lac ', '₹80 Lac ', '₹60 Lac ', '₹60 Lac ', '₹85 Lac ', '₹49 Lac ', '₹55 Lac ', '₹53 Lac ', '₹69 Lac ', '₹1.25 Cr ', '₹87 Lac ', '₹32 Lac ', '₹71 Lac ', '₹85 Lac ', '₹47 Lac ', '₹46 Lac ', '₹43 Lac ', '₹1.15 Cr ', '₹27 Lac ', '₹29.7 Lac ', '₹59 Lac ', '₹45 Lac ', '₹35 Lac ', '₹74 Lac ', '₹39 Lac ', '₹46 Lac ', '₹1.30 Cr ', '₹40 Lac ', '₹70 Lac ', '₹74.9 Lac ', '₹47.5 Lac ', '₹72 Lac ', '₹97 Lac ', '₹63 Lac ', '₹35 Lac ', '₹63.6 Lac ', '₹47 Lac ', '₹1 Cr ', '₹38 Lac ', '₹30 Lac ', '₹97.5 Lac ', '₹30 Lac ', '₹30 Lac ', '₹55 Lac ', '₹60 Lac ', '₹73 Lac ', '₹45 Lac ', '₹89 Lac ', '₹70 Lac ', '₹49 Lac ', '₹43.6 Lac ', '₹56 Lac ', '₹97 Lac ', '₹47 Lac ', '₹78 Lac ', '₹38.9 Lac ', '₹26 Lac ', '₹42 Lac ', '₹38 Lac ', '₹78 Lac ']
['₹4950', '₹6957', '₹4528', '₹7542', '₹5706', '₹7303', '₹5000', '₹11335', '₹8889', '₹6997', '₹8146', '₹20632', '₹5655', '₹6071', '₹9800', '₹6367', '₹5158', '₹6374', '₹4553', '₹4545', '₹7500', '₹12500', '₹7705', '₹5305', '₹7436', '₹5333', '₹7418', '₹10194', '₹4650', '₹26471', '₹23276', '₹16667', nan, '₹23636', '₹37500', '₹27136', '₹18545', '₹60000', '₹5047', nan, '₹11529', '₹38459', '₹7242', nan, '₹10000', '₹14583', '₹5376', '₹12042', '₹13669', '₹19375', '₹21333', '₹33333', '₹22951', '₹10695', '₹25316', '₹5268', '₹8658', '₹5000', '₹4444', '₹8947', nan, '₹4895', '₹6111', '₹5268', '₹5712', '₹8367', '₹8406', nan, '₹5086', '₹9140', '₹6112', '₹7931', '₹4335', '₹11616', '₹4060', '₹3999', '₹7584', '₹5294', '₹6481', '₹6198', '₹4343', '₹6571', '₹11818', '₹5249', '₹6763', '₹8000', '₹3242', '₹5143', '₹4615', '₹4000', '₹4667', '₹4130', '₹4087', '₹7634', '₹3619', '₹3046', '₹7500', '₹5000', '₹3000', '₹3667', '₹3750', '₹4056', '₹3309', '₹5235', '₹4118', '₹3250', '₹6054', '₹5173', '₹4215', '₹4756', '₹3700', '₹2600', '₹4667', '₹4222', '₹7091', nan, nan, nan, nan, nan]
['3', '2', '2', '3', '2', '3', '2', '3', '3', '2', '3', '3', '3', '2', '2', '2', '2', '2', '3', '3', '2', '4', '3', '3', '3', '3', '3', '3', '3', '2', '1', '1', '2', '3', '1', '3', '2', '1', '1', '2', '2', '1', '1', '2', '4', '2', '2', '3', '1', '2', '1', '1', '2', '3', '2', '2', '1', '2', '2', '2', '2', '2', '3', '2', '2', '2', '3', '2', '3', '2', '2', '1', '2', '2', '2', '1', '2', '2', '1', '2', '2', '2', '1', '3', '2', '2', '2', '2', '2', '2', '3', '2', '3', '3', '2', '3', '2', '3', '2', '2', '3', '1', '2', '3', '3', '3', '3', '3', '3', '2', '2', '2', '3', '2', '3', '3', '2', '2', '2', '2']
['1000', '1150', '1060', '1790', '803', '1640', '1250', '1588', '1000', '1465', '1500', '1418', '1350', '1000', '1250', '857', '753', '1186', '1250', '770', '2150', '1900', '1460', '1885', '1950', '1250', '1820', '2060', '740', '331', '310', '610', '1160', '665', '1052', '685', '400', '332', '776', '800', '450', '430', '675', '1870', '820', '850', '1172', '404', '575', '507', '558', '680', '1130', '562', '535', '590', '792', '935', '632', '1044', '800', '1200', '1350', '950', '1001', '900', '800', '852', '1494', '700', '634', '1395', '930', '464', '580', '509', '990', '445', '469', '778', '850', '540', '1194', '898', '700', '600', '762', '1035', '800', '1300', '823', '1700', '1575', '700', '1540', '1150', '1310', '1000', '915', '1300', '600', '1000', '1300', '1516', '1800', '1100', '1620', '1600', '970', '1133', '925', '1875', '1050', '1640', '840', '800', '900', '650', '800', nan]
[' Omkar Pandey', ' gokul', ' Thayumanavan', ' Prasanna', ' Akash Akash', ' av nath', ' Sangeeta Pillai', ' ganga k', ' Anjan K', ' Omesh Saraf', ' Mohd Hussain ', ' Rahul Jain', ' Gaurav Kumar GAURAV', ' Ancy', ' Anoop', ' geetha', ' Omkar Omkar', ' ruchika', ' jeswanth', ' vikas saxena', ' Ranju', ' tezal', ' shravan', ' Bhav', ' Nagananda', ' MD Fuzail', ' ashwanth', ' Partha Sarma', ' Shekar', ' Sashank Constructions', ' K SH', ' NIYATI', ' Sachin Kunder', ' Motashaw Motashaw', ' MONCY BHASKAR', ' Anam khan', ' Milind Desai', ' Jas Baljeet J', ' Gangaram Dhuri', ' Satyajit Satyajit', ' vasant ahir', ' Rajendra', ' Aditi Shah', ' Haroon mansuri', ' kishor kishor', ' Medha Naik', ' Anita', ' jagdish Dassani', ' Raghav Sharma', ' nandan lanjekar', ' amit chalke', ' Arun Iyer', ' Ritu', ' Khuzema Tajir', ' Dipti solanki', ' sujatha', ' Aysha Khan', ' DHRUV SHARMA', ' Mumtaz Nazim', ' Savith Raghavan Savith Raghavan', ' Yogesh Baskaran', ' K VINODH KUMAR', ' GURURAJAN BABU', ' Vijay Jaya', ' siva', ' Prasanna', ' M DINESH KANNA', ' Elango Rajendran', ' Dev', ' vinothan', ' Owner', ' M G S Jayamalathi', ' Mahalingam', ' Kads', ' YOGEESWARAN', ' Kuppuswamy Bj', ' Jayalakshmi', ' Vidhya vidhya', ' Sujith', ' sakthi', ' Santhosh', ' Santhosh Developers', ' R Premalatha', ' Jebaselvan Navaraj', ' Ramkumar Gopalsamy', ' ram', ' Faizal', ' Vimal Kumar S', ' Venugopal', ' PURUSHOTHAMAN', ' Hari', ' Sl Prasanna', ' Sonia', ' Ramana Murthy', ' s bhargavi', ' Lakshman', ' Devi', ' jitendra sai', ' Ram', ' vakada ramana', ' Ravindra Pamidi', ' Praveen', ' Sai Stylish', ' MADHU', ' Uma Chavali', ' TEJARAM THRI', ' Jagan', ' Rakesh Power', ' Maheshwari', ' RAMACHANDRA RAO', ' Jaswanth', ' Srikanth', ' Akshay Kumar', ' swamy m', ' lalitha dhulipala', ' Vision Properties', ' Honey Group', ' New Living Properties', ' podilapu simhachalam', ' Dinakar']
['FloorGround out of 4', 'FloorGround out of 4', 'Floor4 out of 4', 'Floor14 out of 15', 'Floor2 out of 4', 'Floor1 out of 4', 'Floor3 out of 5', 'Basement out of 17', 'Floor3 out of 14', 'Floor2 out of 10', 'FloorGround out of 7', 'Floor16 out of 16', 'FloorGround out of 5', 'Floor1 out of 7', 'Floor2 out of 4', 'Floor2 out of 5', 'Floor3 out of 6', 'Floor1 out of 7', 'Floor1 out of 4', 'Floor6 out of 12', 'Floor9 out of 9', 'Floor1 out of 4', 'Floor2 out of 4', 'Floor5 out of 5', 'Floor2 out of 4', 'Floor1 out of 3', 'Floor13 out of 27', 'Floor5 out of 13', 'Floor3 out of 3', 'FloorGround out of 4', 'Floor8 out of 9', 'Floor1 out of 5', 'Floor5 out of 15', 'Floor4 out of 7', 'FloorGround out of 76', 'Floor4 out of 4', 'FloorGround out of 3', 'Floor1 out of 6', 'Floor5 out of 20', 'Floor6 out of 12', 'Floor7 out of 7', 'FloorGround out of 4', 'Floor1 out of 3', 'Floor21 out of 21', 'Floor3 out of 3', 'Floor7 out of 21', 'Floor5 out of 7', 'Floor5 out of 7', 'Floor4 out of 15', 'Floor9 out of 15', 'Floor17 out of 18', 'Floor5 out of 12', 'Floor7 out of 7', 'Floor15 out of 23', 'Floor3 out of 4', 'Floor2 out of 13', 'Floor2 out of 4', 'Floor1 out of 11', 'Floor1 out of 4', 'Floor1 out of 2', 'FloorGround out of 2', 'Floor2 out of 2', 'Floor1 out of 2', 'Floor1 out of 2', 'Floor1 out of 2', 'Floor4 out of 4', 'Floor3 out of 5', 'Floor16 out of 16', 'Floor3 out of 4', 'Floor1 out of 2', 'Floor4 out of 4', 'Floor3 out of 4', 'Floor1 out of 2', 'Floor2 out of 4', 'Floor2 out of 2', 'Floor2 out of 3', 'Floor2 out of 3', 'Floor1 out of 4', 'Floor1 out of 3', 'Floor1 out of 3', 'Floor1 out of 1', 'FloorGround out of 2', 'Floor1 out of 2', 'Floor1 out of 1', 'Floor1 out of 2', 'Floor1 out of 1', 'Floor1 out of 4', 'Floor1 out of 4', 'Floor2 out of 5', 'Floor10 out of 16', 'Floor15 out of 16', 'FloorGround out of 6', 'Floor2 out of 3', 'Floor3 out of 5', 'Floor4 out of 4', 'Floor1 out of 5', 'Floor2 out of 4', 'Floor2 out of 5', 'Floor2 out of 5', 'Basement out of 3', 'Floor2 out of 5', 'Floor3 out of 5', 'Floor1 out of 5', 'Floor2 out of 5', 'Floor1 out of 5', 'Floor5 out of 5', 'Floor1 out of 5', 'Floor2 out of 4', 'Floor2 out of 10', 'Floor4 out of 5', 'Floor2 out of 3', 'Floor1 out of 5', 'Floor4 out of 5', 'Floor2 out of 5', 'Floor1 out of 5', 'Floor3 out of 5', 'Floor5 out of 5', 'Floor3 out of 5', nan, nan]
[' Kattigenahalli', ' Ayodaya Nagar', ' Electronic City ', ' Tumkur Road ', ' Kudlu Gate ', ' Hal Stage 2', ' Murugeshpalya', ' Begur Road ', ' Judicial Layout', ' Bellandur', ' Kasturi Nagar ', ' Koramangala Block 1 ', ' Sarjapur Road ', ' Devanahalli ', ' Hanumantha Nagar', ' ', ' Doddakannelli ', ' Electronic City ', ' Sarjapur Road ', ' Jigani ', ' ', ' ', ' Jaya Nagar Block 3 ', ' whitefield ', ' whitefield ', ' Harlur ', ' Sarjapur Road ', ' Hoodi', ' Haralur Ma', ' Yelahanka ', ' Daulat Nagar ', ' Samel Pada ', ' Borivali West ', ' Goregaon West ', ' Vasai East ', ' Worli ', ' Kurla East ', ' Andheri East ', ' Kandivali East ', ' Mahim West ', ' Borivali West ', ' Chembur West ', ' ', ' ', ' Dadar West ', ' Boisar West ', ' Mira Road ', ' K', ' Virar West ', ' Vasai West ', ' Bhandup West ', ' Kalyan ', ' Goregaon East ', ' Marol Maroshi Road ', ' Malad West ', ' Vikhroli East ', ' Juhu ', ' Andheri East ', ' Jogeshwari West ', ' Mulund West ', ' Mannivakkam Chennai', ' New Perungalathur Chennai', ' Kovilambakkam Chennai', ' Perumbakkam Chennai', ' Mogappair Chennai', ' Tambaram West Tambaram Chennai', ' Manapakkam Chennai', ' Mogappair Chennai', ' Perumbakkam Chennai', ' Iyyappanthangal Chennai', ' Manapakkam Chennai', ' Madipakkam Chennai', ' Selaiyur Chennai', ' Velachery Chennai', ' Sithalapakkam Chennai', ' Adyar Chennai', ' Shankar Nagar Pammal Chennai', ' Chennai', ' Porur Chennai', ' Chennai', ' Villivakkam Chennai', ' Kundrathur Chennai', ' AGS Colony Velachery Chennai', ' Pallikaranai Chennai', ' Pozhichalur Chennai', ' Jothi Nagar Chitlapakkam Chennai', ' VGP Layout Palavakkam Chennai', ' Perundevi Ammal Nagar Chennai', ' Gu', ' Chennai', ' ', ' ', ' Madhurwada ', ' Pendurthi ', ' Pithapuram Colony ', ' Madhurwada ', ' Pendurthi ', ' Visalakshi Nagar ', ' Balaji Nagar ', ' Sujatha nagar ', ' ', ' Mural', ' Poth', ' Kurmannapalem ', ' Kurmannapalem ', ' Pendurthi ', ' Kurmannapalem ', ' ', ' ', ' Kancharapalem ', ' Kurmannapalem ', ' Railway New Colony ', ' Gajuwaka ', ' Aganampudi ', ' Madhurwada ', ' Tagarapuvalsa ', ' Parawada ', ' PM Palem ', ' Madhurwada ', ' MVP Colony ']
['bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'bangalore', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'mumbai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'chennai', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam', 'visakhapatnam']
120
120
120
120
120
120
120
120


# inserting NaN's at index positions
per_sqt.insert(32,np.nan)
per_sqt.insert(39,np.nan)
per_sqt.insert(43,np.nan)
per_sqt.insert(60,np.nan)
per_sqt.insert(67,np.nan)
per_sqt.append(np.nan)
per_sqt.append(np.nan)
per_sqt.append(np.nan)
per_sqt.append(np.nan)
per_sqt.append(np.nan)


# inserting NaN's at index positions
Floor.append(np.nan)
Floor.append(np.nan)


# inserting NaN's at index positions
Carpet_Area.append(np.nan)


# Creation the Dict with cloumns names as strings and values as column names in which values are present.
df1 = {'City':City,
             'Area':Area,
             'Owner':Owner,
             'BHK':BHK,
             'Floor':Floor,
             'price':price,
             'Carpet_Area':Carpet_Area,
             'per_sqt':per_sqt
}


Project1 = pd.DataFrame(df1)


Project1.head(20)


# conversion of DataFrame to csv
Project1.to_csv('DF1.csv')


# Code for the collection of the data
# here list of 3 cities are taken and collection of data is done for them
cities = ['ranchi','haridwar','vadodara']
price=[]
per_sqt=[]
BHK=[]
Carpet_Area=[]
Owner=[]
Floor=[]
Area=[]
City=[]
for j in cities:
    url = "https://www.magicbricks.com/flats-in-"+j+"-for-sale-pppfs"
    print(url)
    page = requests.get(url)
    soup = BeautifulSoup(page.text)
    
    for i in soup.find_all("div",class_="mb-srp__card__price--amount"):
        price.append(i.text)
        
    for i in soup.find_all("div",class_="mb-srp__card__price--size"):
        per_sqt.append(i.text.split(' ')[0])
        
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        BHK.append(i.text[0])
        
    for i in soup.find_all("div",class_="mb-srp__card__summary--value"):
        a =i.text
        b = re.findall(r"(\d+)\ssqft",str(a))
        if b:
            Carpet_Area.append(b[0])
    for i in soup.find_all('div',class_='mb-srp__card__ads--name'):
        Owner.append(i.text.split(':')[1])    

            
    for i in soup.find_all("div",class_="mb-srp__card__summary__list--item"):
        a = i.text
        b = re.findall(r"\w+\s\w+\s\w+\s\d+",a)
        if b:
            Floor.append(b[0])
            
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        Area.append(i.text.split('in')[1].split(',')[0])
        City.append(j)
    print(i,'---->', len(Owner))
    print(i,'---->', len(Floor))
    print(i,'---->', len(per_sqt))

https://www.magicbricks.com/flats-in-ranchi-for-sale-pppfs
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Hawai Nagar , Ranchi</h2> ----> 30
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Hawai Nagar , Ranchi</h2> ----> 30
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Hawai Nagar , Ranchi</h2> ----> 28
https://www.magicbricks.com/flats-in-haridwar-for-sale-pppfs
<h2 class="mb-srp__card--title">1 BHK Flat for Sale in Sidcul , Haridwar</h2> ----> 60
<h2 class="mb-srp__card--title">1 BHK Flat for Sale in Sidcul , Haridwar</h2> ----> 60
<h2 class="mb-srp__card--title">1 BHK Flat for Sale in Sidcul , Haridwar</h2> ----> 57
https://www.magicbricks.com/flats-in-vadodara-for-sale-pppfs
<h2 class="mb-srp__card--title">4 BHK Flat for Sale in , Vadodara</h2> ----> 90
<h2 class="mb-srp__card--title">4 BHK Flat for Sale in , Vadodara</h2> ----> 89
<h2 class="mb-srp__card--title">4 BHK Flat for Sale in , Vadodara</h2> ----> 83


# inserting NaN's at index positions
Floor.append(np.nan)
Carpet_Area.append(np.nan)


# inserting NaN's at index positions
per_sqt.insert(11,np.nan)
per_sqt.insert(25,np.nan)
per_sqt.insert(53,np.nan)
per_sqt.insert(71,np.nan)
per_sqt.insert(72,np.nan)
per_sqt.insert(79,np.nan)
per_sqt.insert(84,np.nan)


# printing to see the values are obtained are not
print(price)
print(per_sqt)
print(BHK)
print(Carpet_Area)
print(Owner)
print(Floor)
print(Area)
print(City)

# Finding the length to see is all the lengths are same
print(len(price))
print(len(per_sqt))
print(len(BHK))
print(len(Carpet_Area))
print(len(Owner))
print(len(Floor))
print(len(Area))
print(len(City))

['₹62 Lac ', '₹76.4 Lac ', '₹77 Lac ', '₹31.5 Lac ', '₹68.6 Lac ', '₹75 Lac ', '₹40 Lac ', '₹58 Lac ', '₹60 Lac ', '₹56 Lac ', '₹51 Lac ', '₹77 Lac ', '₹31 Lac ', '₹33 Lac ', '₹1.15 Cr ', '₹68 Lac ', '₹82 Lac ', '₹32 Lac ', '₹58 Lac ', '₹45 Lac ', '₹45 Lac ', '₹45 Lac ', '₹1.25 Cr ', '₹40 Lac ', '₹48 Lac ', '₹1.05 Cr ', '₹60 Lac ', '₹91 Lac ', '₹48 Lac ', '₹1.25 Cr ', '₹41 Lac ', '₹77 Lac ', '₹23.5 Lac ', '₹58 Lac ', '₹14.3 Lac ', '₹1.90 Cr ', '₹38 Lac ', '₹32 Lac ', '₹50.5 Lac ', '₹47 Lac ', '₹28 Lac ', '₹55 Lac ', '₹40 Lac ', '₹35 Lac ', '₹48 Lac ', '₹58 Lac ', '₹16.5 Lac ', '₹25 Lac ', '₹20 Lac ', '₹16 Lac ', '₹67 Lac ', '₹25 Lac ', '₹28 Lac ', '₹37 Lac ', '₹40 Lac ', '₹66 Lac ', '₹14 Lac ', '₹34 Lac ', '₹65 Lac ', '₹22 Lac ', '₹60 Lac ', '₹80 Lac ', '₹22 Lac ', '₹31 Lac ', '₹25 Lac ', '₹23 Lac ', '₹15.5 Lac ', '₹45 Lac ', '₹79 Lac ', '₹45 Lac ', '₹30 Lac ', '₹7.5 Lac ', '₹45 Lac ', '₹35 Lac ', '₹65 Lac ', '₹47 Lac ', '₹19.5 Lac ', '₹25 Lac ', '₹31 Lac ', '₹35 Lac ', '₹25 Lac ', '₹90 Lac ', '₹50 Lac ', '₹82 Lac ', '₹19 Lac ', '₹60 Lac ', '₹8 Lac ', '₹27 Lac ', '₹42 Lac ', '₹1.20 Cr ']
['₹4655', '₹4200', '₹5325', '₹5497', '₹4400', '₹3731', '₹3774', '₹4531', '₹4317', '₹3733', '₹3682', nan, '₹2897', '₹3986', '₹5476', '₹5207', '₹5325', '₹2712', '₹4629', '₹4110', '₹4039', '₹4274', '₹5556', '₹3448', '₹3840', nan, '₹4484', '₹5170', '₹4800', '₹8333', '₹2522', '₹4529', '₹2901', '₹5273', '₹2192', '₹5429', '₹3140', '₹2960', '₹3483', '₹3167', '₹2605', '₹3963', '₹3636', '₹3500', '₹3967', '₹4531', '₹3113', '₹3846', '₹2857', '₹2133', '₹4295', '₹3125', '₹3230', nan, '₹3687', '₹3667', '₹2318', '₹3716', '₹3824', '₹2933', '₹2953', '₹5333', '₹2444', '₹3875', '₹2941', '₹3286', '₹2053', '₹2970', '₹4158', '₹2093', '₹2542', nan, nan, '₹3182', '₹3683', '₹3298', '₹1696', '₹2551', '₹2818', nan, '₹2174', '₹3817', '₹5556', '₹3216', nan, '₹2521', '₹4000', '₹3000', '₹2545', '₹4615']
['3', '3', '3', '1', '3', '3', '2', '3', '3', '3', '3', '3', '2', '2', '3', '3', '3', '2', '2', '2', '2', '3', '4', '2', '3', '5', '3', '3', '2', '3', '2', '3', '2', '3', '1', '2', '3', '2', '2', '3', '2', '3', '2', '2', '3', '2', '1', '1', '1', '1', '4', '2', '1', '2', '2', '3', '1', '2', '3', '1', '3', '3', '2', '2', '2', '1', '1', '2', '3', '4', '2', '1', '2', '2', '3', '3', '2', '2', '2', '2', '2', '3', '3', '3', '2', '4', '1', '2', '3', '4']
['1110', '1500', '1150', '573', '1560', '2010', '930', '1280', '1250', '1500', '1385', '1212', '1000', '800', '1925', '1306', '1540', '1180', '971', '950', '1114', '1053', '2250', '950', '1150', '1650', '1338', '1760', '968', '1500', '1490', '750', '1100', '650', '3000', '1210', '719', '1450', '1484', '1075', '1388', '1025', '890', '1050', '500', '530', '650', '700', '650', '1600', '800', '867', '1450', '900', '1800', '604', '915', '1700', '750', '2032', '1500', '900', '780', '1208', '700', '755', '1515', '1900', '1465', '1080', '450', '1010', '850', '1016', '1025', '800', '980', '800', '640', '1150', '2358', '900', '2350', '750', '1580', '200', '600', '1650', '2600', nan]
[' Waseem Ahmad', ' Anuj Krchandra', ' Rabindra Bakshi', ' Abhishek Gupta', ' Shree Ram Developers', ' vivek chandra', ' sushant Bhattacharjee', ' Ritesh Nagpal', ' Nitin Kumar', ' PARTHO BOSE', ' Pradeep', ' rajesh singh', ' ashish singh', ' sanjeev', ' Kshav', ' Abhishek', ' Neeraj', ' Manit Kumar', ' Mukesh Mukesh', ' Samita Chatterjee', ' shakuntala verma', ' vikash singh', ' Avishek Modi Modi', ' Amar nath Pandey', ' MANISH KUMAR', ' taha', ' Rabish Kumar', ' Pintu Singh', ' Ravi Prasad', ' Rajiv Ranjan', ' Kiran Kumar Patel', ' SHREYA SAGAR', ' Krrish', ' Rajendra Wadhwa', ' Shakti Verma', ' Mridul Puri', ' Manish', ' Ajay', ' tarun agarwal', ' Arvind Bhardwaj', ' Jasjit Pannu', ' Abhishek Chauhan', ' sandeep', ' &quot;Magicbricks User&quot;', ' Dinesh Kukreti', ' Shalinirahal Banga', ' Alok Singh', ' saba b Khan', ' Rahul kumar', ' SANDEEP KUMAR', ' Sandeep Chaturvedi', ' AYUSH SAXENA', ' puneet', ' Abhishek chaudhary', ' Rishabh Kapoor', ' Surendra Satija', ' Harendra Kumar', ' chandra', ' Rohit', ' Dharmveer', ' Samy', ' Sudhanshu Kavthekar', ' Jaimin Parmar ', ' paresh', ' Mayur Patel', ' Parimal Patel', ' Rakesh', ' Bhavik Bhavik Mehta', ' Jyotsna Khoda', ' raghuvirsinh Vaghela', ' Pratik gosaliya', ' Umakant parmar', ' Akshat Dani', ' Nimisha', ' Hitansh Agrawal', ' anil', ' mitul patel', ' anil makvana', ' Manubhai Shah', ' Shashank', ' Pushpita Roy Choudhury', ' namita Namita', ' Deepak Singh', ' Manish Nagarajan', ' Ankur Javia', ' Jigar', ' Maheshbhai Dhokia', ' Aniruddha', ' Bindi shah', ' Jiten chokshi']
['Floor3 out of 4', 'Floor5 out of 6', 'Floor4 out of 4', 'Floor2 out of 4', 'Floor8 out of 12', 'Floor2 out of 4', 'Floor1 out of 4', 'Floor2 out of 7', 'Floor4 out of 4', 'Floor1 out of 1', 'Floor3 out of 4', 'Floor4 out of 6', 'Floor1 out of 4', 'Floor3 out of 3', 'Floor9 out of 10', 'Floor4 out of 4', 'Floor4 out of 5', 'Floor3 out of 4', 'FloorGround out of 6', 'Floor3 out of 4', 'Floor3 out of 3', 'Floor1 out of 11', 'Floor3 out of 4', 'Floor3 out of 3', 'Floor2 out of 3', 'FloorGround out of 8', 'Floor1 out of 4', 'Floor4 out of 4', 'Floor1 out of 4', 'Floor4 out of 8', 'FloorGround out of 4', 'FloorGround out of 7', 'Floor6 out of 8', 'Floor1 out of 5', 'Floor1 out of 4', 'FloorGround out of 2', 'Floor2 out of 7', 'Floor3 out of 7', 'Floor6 out of 6', 'Floor1 out of 1', 'Floor4 out of 6', 'Floor2 out of 5', 'FloorGround out of 4', 'Floor3 out of 7', 'Floor5 out of 7', 'FloorGround out of 1', 'FloorGround out of 3', 'FloorGround out of 4', 'Floor3 out of 4', 'Floor1 out of 4', 'Floor2 out of 4', 'Floor3 out of 3', 'Floor4 out of 4', 'Floor3 out of 3', 'Floor5 out of 12', 'Floor4 out of 5', 'Floor2 out of 2', 'Floor3 out of 7', 'Floor2 out of 7', 'Floor4 out of 6', 'Floor5 out of 5', 'Floor3 out of 4', 'Floor3 out of 4', 'Floor1 out of 3', 'Floor5 out of 5', 'Floor2 out of 4', 'Floor6 out of 8', 'Floor3 out of 4', 'Floor4 out of 5', 'Floor1 out of 5', 'Floor3 out of 4', 'Floor1 out of 3', 'Floor3 out of 5', 'Floor5 out of 12', 'Floor4 out of 9', 'Floor2 out of 5', 'Floor2 out of 8', 'Floor1 out of 3', 'Floor4 out of 5', 'Floor4 out of 4', 'Floor4 out of 9', 'Floor5 out of 5', 'Floor9 out of 9', 'Floor3 out of 4', 'Floor5 out of 5', 'Floor2 out of 4', 'Floor1 out of 5', 'Floor3 out of 5', 'Floor2 out of 2', nan]
[' H', ' ', ' S', ' Ratu Road ', ' Harihar S', ' Bariatu Road ', ' Morabadi ', ' Bahu Bazar ', ' ', ' Vikas Nagar ', ' Bariatu ', ' Hesag ', ' ', ' Barga', ' Sector 2 Masibari ', ' Upper Chutia ', ' Hazaribag Road ', ' Namkum ', ' Lalpur ', ' Hesag ', ' Kokar ', ' BIT Mesra ', ' ', ' Harihar S', ' H', ' ', ' Namkum ', ' ', ' Bariatu Road ', ' Hawai Nagar ', ' Sidcul ', ' Roshanabad ', ' Sidcul ', ' Devpura ', ' Laksar Road ', ' Arya Nagar ', ' Jwalapur ', ' Roshanabad ', ' Patanjali ', ' Rajaji National Park ', ' Roshanabad ', ' Jwalapur ', ' Jwalapur ', ' Jwalapur ', ' ', ' Roorkee ', ' Patanjali ', ' ', ' Shivalik Nagar ', ' Shantikunj ', ' Haripur Kalan ', ' ', ' Kankhal ', ' Jwalapur ', ' Patanjali ', ' NH-58 ', ' Jwalapur ', ' Sidcul ', ' NH-58 ', ' Sidcul ', ' Vasna Bhayli Ma', ' Race Course circle ', ' Ajwa Road ', ' ', ' Makar Pura ', ' Chhani ', ' Suryanagar ', ' Gotri Road Gotri ', ' Vasant Vihar ', ' Sevasi ', ' Chhani ', ' Nani Bapod ', ' Race Course circle ', ' Kalali ', ' Sama Savli Road ', ' Gorwa ', ' Bill ', ' Vasna Bhayli Ma', ' Subhanpura ', ' Bhayli ', ' Danteshwar ', ' Vasna Bhayli Ma', ' Gotri Sevasi Road ', ' Harni Road ', ' Sayajipura ', ' Bill ', ' ', ' Vasna Bhayli Ma', ' ', ' ']
['ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'ranchi', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'haridwar', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara', 'vadodara']
90
90
90
90
90
90
90
90


# Creation the Dict with cloumns names as strings and values as column names in which values are present.
df2 = {'City':City,
             'Area':Area,
             'Owner':Owner,
             'BHK':BHK,
             'Floor':Floor,
             'price':price,
             'Carpet_Area':Carpet_Area,
             'per_sqt':per_sqt
}


# Creation of DataFrame
Project2=pd.DataFrame(df2)


Project2.head(20)


# conversion of DataFrame to csv
Project2.to_csv('DF2.csv')


# Code for the collection of the data
# here list of 7 cities are taken and collection of data is done for them
cities = ['greater-noida','gurgaon','mangalore','raipur','agra','bhiwadi','mysore']
City = []
Area=[]
Owner=[]
BHK=[]
Floor=[]
price=[]
Carpet_Area=[]
per_sqt=[]
for j in cities:
    url = "https://www.magicbricks.com/flats-in-"+j+"-for-sale-pppfs"
    print(url)
    page = requests.get(url)
    soup = BeautifulSoup(page.text)
    
    for i in soup.find_all("div",class_="mb-srp__card__price--amount"):
        price.append(i.text)
        
    for i in soup.find_all("div",class_="mb-srp__card__price--size"):
        per_sqt.append(i.text.split(' ')[0])
        
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        BHK.append(i.text[0])
        
    for i in soup.find_all("div",class_="mb-srp__card__summary--value"):
        a =i.text
        b = re.findall(r"(\d+)\ssqft",str(a))
        if b:
            Carpet_Area.append(b[0])
    for i in soup.find_all('div',class_='mb-srp__card__ads--name'):
        Owner.append(i.text.split(':')[1])    

            
    for i in soup.find_all("div",class_="mb-srp__card__summary__list--item"):
        a = i.text
        b = re.findall(r"\w+\s\w+\s\w+\s\d+",a)
        if b:
            Floor.append(b[0])
            
    for i in soup.find_all("h2",class_="mb-srp__card--title"):
        Area.append(i.text.split('in')[1].split(',')[0])
        City.append(j)
    print(i,'---->', len(Owner))
    print(i,'---->', len(Floor))
    print(i,'---->', len(per_sqt))

https://www.magicbricks.com/flats-in-greater-noida-for-sale-pppfs
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Yamuna Expressway , Greater Noida</h2> ----> 29
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Yamuna Expressway , Greater Noida</h2> ----> 31
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Yamuna Expressway , Greater Noida</h2> ----> 28
https://www.magicbricks.com/flats-in-gurgaon-for-sale-pppfs
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Sector 65 , Gurgaon</h2> ----> 59
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Sector 65 , Gurgaon</h2> ----> 64
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Sector 65 , Gurgaon</h2> ----> 56
https://www.magicbricks.com/flats-in-mangalore-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yeyyadi Indl. Estate , Mangalore</h2> ----> 89
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yeyyadi Indl. Estate , Mangalore</h2> ----> 93
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Yeyyadi Indl. Estate , Mangalore</h2> ----> 82
https://www.magicbricks.com/flats-in-raipur-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Daldal Seoni , Raipur</h2> ----> 119
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Daldal Seoni , Raipur</h2> ----> 123
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Daldal Seoni , Raipur</h2> ----> 112
https://www.magicbricks.com/flats-in-agra-for-sale-pppfs
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Rakabganj , Agra</h2> ----> 149
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Rakabganj , Agra</h2> ----> 153
<h2 class="mb-srp__card--title">3 BHK Flat for Sale in Rakabganj , Agra</h2> ----> 142
https://www.magicbricks.com/flats-in-bhiwadi-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Alwar Bypass Road , Bhiwadi</h2> ----> 179
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Alwar Bypass Road , Bhiwadi</h2> ----> 184
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Alwar Bypass Road , Bhiwadi</h2> ----> 172
https://www.magicbricks.com/flats-in-mysore-for-sale-pppfs
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Sriramapura , Mysore</h2> ----> 209
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Sriramapura , Mysore</h2> ----> 213
<h2 class="mb-srp__card--title">2 BHK Flat for Sale in Sriramapura , Mysore</h2> ----> 198


# printing to see the values are obtained are not
print(City)
print(Area)
print(BHK)
print(Carpet_Area)
print(Owner)
print(price)
print(per_sqt)
print(Floor)

# Finding the length to see is all the lengths are same
print(len(City))
print(len(Area))
print(len(BHK))
print(len(Carpet_Area))
print(len(Owner))
print(len(price))
print(len(per_sqt))
print(len(Floor))

['greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'greater-noida', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'gurgaon', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'mangalore', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'raipur', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'agra', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'bhiwadi', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore', 'mysore']
[' Zeta 1 ', ' Sector P4 ', ' Eta 2', ' Sector 3 ', ' Zeta 1 ', ' Omicron ', ' Greater Noida West ', ' Eta 2', ' Yamuna Expressway ', ' Greater Noida West ', ' ', ' Chi 5 ', ' Jaypee Greens ', ' Zeta 1 ', ' Greater Noida West ', ' Omicron 1 ', ' Greater Noida West ', ' Sector 1 Greater Noida West ', ' Greater Noida West ', ' Greater Noida West ', ' Greater Noida West ', ' ', ' Noida Extention ', ' Sector 1 Greater Noida West ', ' Omicron 3 ', ' UPSIDC Site C Block H ', ' Sector 16 ', ' Chi 5 ', ' Yamuna Expressway ', ' Yamuna Expressway ', ' Jal Vayu Vihar', ' Sohna Sector 32 ', ' Kendriya Vihar', ' Sector 23 ', ' Sohna Sector 35 ', ' Sector 84 ', ' Sector 104 ', ' Sector 78 ', ' Sector 112 ', ' Sector 102 ', ' Sector 48 ', ' Sector 7 ', ' Sector 55 ', ' DLF City Phase 1 ', ' Sector 57 ', ' New Colony', ' Sohna Sector 33 ', ' Sector 83 ', ' Sector 47 ', ' Sector 37C', ' Sector 9 ', ' DLF City Phase 1 ', ' Sector 4 ', ' South City 2', ' New Palam Vihar', ' Sector 54 ', ' Patel Nagar', ' Sector 77', ' Sector 65 ', ' Sector 65 ', ' Shakti Nagar ', ' Urwa ', ' Bunts Hostel Road ', ' Urwa ', ' Kulshekar ', ' Kulai ', ' Bajpe ', ' Ujire ', ' Bondel ', ' Kateel ', ' K', ' Shakti Nagar ', ' Falnir ', ' Kulai ', ' Kadri ', ' Padil ', ' Kulai ', ' Urwa ', ' Falnir ', ' Jepp', ' Derebail ', ' Mulky ', ' ', ' Pandeshwar ', ' Ashok Nagar ', ' Konchady Cross Road ', ' Pumpwell ', ' Mukka ', ' Nandigudda ', ' Yeyyadi Indl. Estate ', ' Daldal Seoni ', ' Mowa ', ' Avanti Vihar ', ' Shankar Nagar ', ' Santoshi Nagar ', ' Naya Raipur ', ' Hirapur Road ', ' VIP Road ', ' Mathpurena ', ' Boria Kalan ', ' Amlidhi ', ' Bhatagaon ', ' Kabir Nagar ', ' Shankar Nagar ', ' Hirapur Road ', ' NH 6 ', ' Shankar Nagar ', ' Deopuri ', ' Kota ', ' Amlidhi ', ' Hirapur Road ', ' Santoshi Nagar ', ' Boria Kalan ', ' Shankar Nagar ', ' Amleshwar ', ' Amlidhi ', ' Kachna Road ', ' Mowa ', ' Amanaka ', ' Daldal Seoni ', ' Sikandra ', ' Vibhav Nagar ', ' Vijay Nagar Colony ', ' Civil L', ' Dayal Bagh ', ' Civil L', ' Shastri Puram ', ' Agra Shamshabad Raja Kherah Marg ', ' Dayal Bagh ', ' Dayal Bagh ', ' Dayal Bagh ', ' Fatehabad Road ', ' Agra Shamshabad Raja Kherah Marg ', ' Sikandra ', ' Sikandra ', ' Dayal Bagh ', ' Sikandra ', ' Fatehabad Road ', ' Shastri Puram ', ' Shastri Puram ', ' Fatehabad Road ', ' Sikandra ', ' Shahganj ', ' Fatehabad Road ', ' Gwalior Road ', ' Agra Shamshabad Raja Kherah Marg ', ' Sector 16B Awas Vikas Colony ', ' Fatehabad Road ', ' Shastri Puram ', ' Rakabganj ', ' Alwar Bypass Road ', ' ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Bhiwadi Mod ', ' Tapukara ', ' Alwar Bhiwadi Road ', ' Vasundhara Nagar ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Rampura ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Vasundhara Nagar ', ' Tapukara ', ' Alwar Bypass Road ', ' Alwar Bypass Road ', ' Gokulam ', ' Bogadi ', ' JP Nagar ', ' Yadavgiri ', ' Yadavgiri ', ' Hebbal ', ' Vijayanagar 4th Stage ', ' Bogadi ', ' Sharadadevi Nagar ', ' Alanahalli ', ' Vijaynagar 3rd Stage ', ' Belagola ', ' ', ' Visveshwara Nagar ', ' ', ' Siddhartha Layout ', ' Vidyarayanapuram ', ' Srirangapatnam ', ' JP Nagar ', ' Bogadi ', ' Chamarajapura ', ' Metagalli ', ' Jaya Laxmi Puram ', ' Hebbal 2nd Stage ', ' Lakshmipuram ', ' ', ' Kuvempunagar ', ' ', ' V V Mohalla ', ' Sriramapura ']
['2', '3', '3', '2', '2', '3', '4', '2', '1', '2', '2', '4', '4', '4', '2', '2', '3', '2', '3', '2', '2', '2', '3', '2', '3', '2', '3', '3', '2', '3', '2', '3', '3', '2', '1', '3', '2', '3', '3', '4', '3', '3', '3', '4', '3', '3', '2', '3', '3', '2', '4', '2', '4', '4', '2', '3', '3', '4', '3', '3', '2', '2', '3', '2', '3', '2', '4', ' ', '3', '2', '1', '2', '3', '2', '2', '3', '3', '2', '2', '2', '2', '2', '2', '2', '3', '2', '3', '2', '3', '2', '2', '3', '3', '2', '3', '2', '3', '2', '2', '2', '2', '2', '1', '2', '2', '3', '2', '3', '3', '2', '3', '3', '3', '3', '3', '2', '3', '2', '1', '2', '2', '3', '2', '2', '5', '2', '2', '3', '3', '3', '3', '2', '2', '2', '2', '3', '5', '3', '2', '2', '3', '3', '2', '3', '2', '2', '2', '3', '2', '3', '2', '2', '2', '1', '2', '2', '2', '2', '1', '1', '3', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '3', '3', '2', '1', '2', '3', '4', '2', '2', '2', '1', '2', '3', '4', '2', '2', '4', '2', '2', '2', '2', '3', '3', '3', '3', '2', '3', '3', '2', '3', '2', '2', '2', '2', '2']
['1200', '1120', '995', '970', '895', '1775', '2364', '660', nan, '950', '995', '3210', '3441', '2200', '910', '840', '1530', '862', '1055', '615', '1155', '950', '840', '870', '1395', '1115', '1285', '1494', '1115', '13750', '900', '1789', '1500', '750', '310', '1750', '570', '1657', '3763', '1800', '1300', '900', '1950', '1556', '1217', nan, '610', '1745', '2470', '10372', '1450', '1250', '1550', '1800', '550', '3650', '960', '1500', '1500', '3069', '670', '1100', '1700', '1275', '1330', '906', '3030', nan, '1390', '1260', '660', '1166', '3900', '975', '1390', '1070', '1870', '1040', '1000', '1320', '783', '72', '1000', '1330', '1356', '884', '1613', '1100', '1341', '1200', '970', '1200', '1985', '840', '1250', '943', '799', '900', '1050', '487', '700', '815', '505', '866', '721', '1200', '1030', '1500', '1805', '1230', '965', '1250', '1337', '960', '1200', '1000', '1500', '860', '550', '906', '900', '1540', '1250', '850', nan, '1050', '848', '1350', '1050', '1400', nan, '1192', '1115', '1000', '660', '1675', '2410', nan, '862', '1250', '1400', '1800', '1750', '1675', '960', '940', '1425', '926', '861', '1500', '1165', '555', '980', '466', '1234', '750', '780', '650', '630', '600', '1430', '660', '815', '1250', '1170', '1111', '1000', '1150', '829', '700', '950', '1250', '1150', '599', '750', '1590', '1522', '500', '630', '790', '1650', '1487', '1095', '9', '1280', '400', '1400', '1174', '650', '1050', '890', '2000', '911', '1280', '800', '1095', '1553', '1875', '1681', '1400', '1019', '2200', '1380', '1100', '1650', '1070', '1080', '1200', '900', '1115']
[' RAHUL TRIVEDI', ' R Bhattacharjee', ' Ashutosh', ' Sandeep gautam', ' vijay', ' PRASAD', ' Kamal Bansal', ' Avik Dey', ' Harkesh Sharma', ' Bhagat Singh', ' Mangat Rai', ' AN SINHA', ' Nihit', ' Shiv Kumar', ' Right Value Solutions', ' Adhunik Propmart', ' Homes And Desire', ' Investors Lab', ' Vardhman Reality', ' piyush nair', ' A Plus Associates', ' Bricksnwall Innovations Pvt.Ltd.', ' Pragati ', ' Prof Arora', ' Prabhat Sharma', ' Gaurav', ' sanjeev', ' Mohd Sarfaraz', ' Atif', ' Sandeep Jain', ' Mohit', ' Nithin Abraham', ' Arun Yadav', ' Lionel Richie', ' Shubham Solanki', ' Anshul', ' Ajay Sharma', ' Pushpendra Sethi', ' Y S Dwivedi', ' Rahul Arora', ' Devesh', ' RAHUL', ' Kapur', ' sanjay kaushik', ' Naresh', ' Rahul', ' Karun', ' Gurdeep Singh', ' Pradeep Sharma', ' Ravinder Chauhan', ' Axiom Landbase Pvt. Ltd.', ' Jk Batra', ' sunil yadav', ' Rishabh Arora', ' Pankaj Ahlawat', ' VIVEK SHARMA', ' Real Estate Gurgaon', ' Akhilesh Sharma ', ' Ankush', ' Manjunath S', ' Govinda', ' Varun Nayak', ' Jeanson Veigas', ' Chethan', ' Naveen Kamath', ' Devdhar Shetty', ' Gopal', ' Stany', ' Brahmari Vilas', ' Baptist Dcunha', ' Priya', ' Iqbal ', ' Santosh Babu Salian', ' Hassan', ' Rajesh Antony Fernandes', ' jaysen', ' Govinda Sharma', ' hashura', ' Lance', ' Arwin', ' sharath kumar', ' Sameer', ' Sabeena Farhath', ' Suman Kumar', ' pkumar', " James D'SA", ' roshan', ' Sunil Veigas', ' SNEHA', ' D Suryaprakash', ' Bhumika Bansal', ' carlus', ' Vaibhav Shukla', ' Khushboo Kashyap', ' Deekaha', ' Divya Singh', ' Arpit Jain', ' Mahesh', ' geeta shrivastava', ' ajit singh', ' Prem Pvt Ltd', ' vijay', ' Siddharth Buildcon', ' Kusum kumari', ' Priyesh Gupta', ' Devinder Singh', ' womesh womesh', ' sandip Agarwal', ' Chainika', ' Karnjeet Biswas', ' TEJESHWARI SAHU', ' Mitchell', ' Angad Lalwani', ' achyutananda', ' Manoj mati', ' vidyadhar behra', ' Sumit Wadhwani', ' Saurabh upadhyay', ' Shubham Soni', ' Vikas Gulati', ' vishal sharma', ' Arnav Singh', ' Vinod Kumar', ' Deepika Chahar', ' Shikha', ' Shubham Kumar', ' hemant sharma', ' Prem lal Taneja', ' Yatendra kumar mehrotra', ' Umang Mathur', ' Akshit Rajoriya', ' Sanjeev Singh', ' nitin singh', ' ajay kumar', ' Divya pandey', ' GALAXY PRINT PACK', ' SHWETANG SHARMA', ' PRAVEEN KUMAR', ' Sapna', ' Ganpati World', ' Shrey Shankar', ' Rakesh Agarwal ', ' Sanchit gupta', ' PRASHANT', ' adil hussain', ' Mohini Sharma', ' vivek jain', ' VIMAL TIWARI', ' Dr Archika Gupta Dr Archika', ' Amit Kumar Saini', ' Radha Kanha', ' M K Realtech', ' R K Real Estate', ' Vikram', ' Amita Singh', ' Raj Singh', ' Dharm Chauhan', ' Sunil Kumar', ' vineet', ' Sandeep singh', ' Ashok Sood', ' munish', ' Brahmprakash', ' Satish', ' Sachinrajput', ' Sunil Kumar', ' Shailender', ' Sumit Kumar', ' Rahul Rijhwani', ' darshan kumar', ' Chandresh', ' Rahul Chauhan', ' Narendra Kumar Agarwal', ' Vivek Anand Semwal', ' Vibhu', ' SANCHITA', ' kuldeep tomer', ' Parveen khanna', ' Pardeep N/A ', ' Vijay', ' Ravi Nambiar', ' Pick Your Prop Estates LLP', ' Veena', ' Amar Kumar Prasad', ' Jagadeesha', ' Meghana', ' Shashi', ' Satish Krishna', ' ARUN', ' subbareddy subbareddy', ' Mallesh', ' Karthik', ' Nanda', ' Mohammed Mansoor', ' Shriya Mankani', ' Nataraj D M', ' Narasimha murthy', ' Vijay Menon', ' Prakash', ' G S ANANTHA', ' Gowrish Bhaskar', ' Ramachandra Murthy', ' Thulaseedharan pillai A', ' Rathan', ' sheelavathi', ' Nayana A.S', ' Srikanth', ' MANJUNATHA', ' Chakko', nan]
['₹47.5 Lac ', '₹46 Lac ', '₹37 Lac ', '₹24 Lac ', '₹48 Lac ', '₹65.4 Lac ', '₹1 Cr ', '₹27 Lac ', '₹15 Lac ', '₹35 Lac ', '₹65 Lac ', '₹2.10 Cr ', '₹3.31 Cr ', '₹98 Lac ', '₹43.5 Lac ', '₹48.5 Lac ', '₹80 Lac ', '₹61.2 Lac ', '₹56 Lac ', '₹50.5 Lac ', '₹63 Lac ', '₹25.5 Lac ', '₹68.9 Lac ', '₹23 Lac ', '₹55 Lac ', '₹44 Lac ', '₹51 Lac ', '₹65 Lac ', '₹33 Lac ', '₹50 Lac ', '₹80 Lac ', '₹1.80 Cr ', '₹1.50 Cr ', '₹45 Lac ', '₹17 Lac ', '₹93 Lac ', '₹35 Lac ', '₹81.1 Lac ', '₹4.60 Cr ', '₹2.60 Cr ', '₹1.20 Cr ', '₹54 Lac ', '₹1.40 Cr ', '₹1.85 Cr ', '₹99 Lac ', '₹85 Lac ', '₹42 Lac ', '₹1 Cr ', '₹3.50 Cr ', '₹93 Lac ', '₹1.20 Cr ', '₹1.32 Cr ', '₹1.60 Cr ', '₹2.35 Cr ', '₹28 Lac ', '₹10.90 Cr ', '₹46 Lac ', '₹1.50 Cr ', '₹2.28 Cr ', '₹5 Cr ', '₹22 Lac ', '₹79 Lac ', '₹69.5 Lac ', '₹58 Lac ', '₹54.8 Lac ', '₹65 Lac ', '₹2 Cr ', '₹10.1 Lac ', '₹65 Lac ', '₹35 Lac ', '₹19 Lac ', '₹40 Lac ', '₹1.80 Cr ', '₹65 Lac ', '₹75 Lac ', '₹48.5 Lac ', '₹90 Lac ', '₹79 Lac ', '₹65 Lac ', '₹50 Lac ', '₹60 Lac ', '₹35 Lac ', '₹32 Lac ', '₹75 Lac ', '₹1.01 Cr ', '₹40 Lac ', '₹1.20 Cr ', '₹45 Lac ', '₹68.5 Lac ', '₹48 Lac ', '₹25 Lac ', '₹77 Lac ', '₹68 Lac ', '₹52 Lac ', '₹29.1 Lac ', '₹31.8 Lac ', '₹32.5 Lac ', '₹30 Lac ', '₹42 Lac ', '₹7.5 Lac ', '₹23 Lac ', '₹31.5 Lac ', '₹14 Lac ', '₹35 Lac ', '₹25 Lac ', '₹33 Lac ', '₹30 Lac ', '₹33.3 Lac ', '₹1.35 Cr ', '₹49 Lac ', '₹30 Lac ', '₹29.1 Lac ', '₹33 Lac ', '₹43.4 Lac ', '₹25 Lac ', '₹36 Lac ', '₹42.9 Lac ', '₹20 Lac ', '₹12.5 Lac ', '₹25 Lac ', '₹25.5 Lac ', '₹43.5 Lac ', '₹68 Lac ', '₹35 Lac ', '₹1.35 Cr ', '₹65 Lac ', '₹44.4 Lac ', '₹33.5 Lac ', '₹42 Lac ', '₹48 Lac ', '₹47 Lac ', '₹40.5 Lac ', '₹28 Lac ', '₹35 Lac ', '₹17.5 Lac ', '₹60 Lac ', '₹81.9 Lac ', '₹55 Lac ', '₹40 Lac ', '₹42 Lac ', '₹44 Lac ', '₹65 Lac ', '₹59.9 Lac ', '₹51 Lac ', '₹24 Lac ', '₹44 Lac ', '₹65 Lac ', '₹24 Lac ', '₹41.3 Lac ', '₹90 Lac ', '₹38 Lac ', '₹12 Lac ', '₹23 Lac ', '₹13 Lac ', '₹36 Lac ', '₹18 Lac ', '₹23 Lac ', '₹13.5 Lac ', '₹11 Lac ', '₹16.8 Lac ', '₹37 Lac ', '₹28 Lac ', '₹15 Lac ', '₹18.8 Lac ', '₹28 Lac ', '₹17.9 Lac ', '₹19 Lac ', '₹27 Lac ', '₹32.5 Lac ', '₹14.5 Lac ', '₹21 Lac ', '₹30 Lac ', '₹22 Lac ', '₹20 Lac ', '₹20 Lac ', '₹59 Lac ', '₹50 Lac ', '₹10 Lac ', '₹18 Lac ', '₹30 Lac ', '₹80 Lac ', '₹60 Lac ', '₹45 Lac ', '₹68 Lac ', '₹95 Lac ', '₹58 Lac ', '₹82 Lac ', '₹58 Lac ', '₹50 Lac ', '₹85 Lac ', '₹58 Lac ', '₹1.01 Cr ', '₹60 Lac ', '₹56 Lac ', '₹48 Lac ', '₹65.7 Lac ', '₹60 Lac ', '₹75 Lac ', '₹75 Lac ', '₹70 Lac ', '₹52 Lac ', '₹1.45 Cr ', '₹85 Lac ', '₹1.63 Cr ', '₹80 Lac ', '₹40 Lac ', '₹69 Lac ', '₹60 Lac ', '₹60 Lac ', '₹72 Lac ']
['₹3958', '₹3286', nan, '₹3719', '₹2474', '₹5363', nan, '₹4230', '₹4091', '₹4805', '₹3684', '₹6533', '₹6542', '₹9001', '₹3912', '₹4780', '₹4663', '₹5229', '₹5322', '₹5308', '₹4630', '₹5020', '₹2429', '₹4700', '₹2370', '₹3943', '₹3946', '₹4351', '₹2960', '₹3636', '₹8889', '₹10061', nan, '₹5294', '₹3864', '₹4439', '₹5000', '₹4894', '₹12224', '₹9630', '₹8000', '₹5143', '₹7179', '₹11889', '₹8135', '₹8213', '₹4421', '₹5731', '₹14170', '₹6503', '₹7500', '₹9429', '₹7111', '₹11190', '₹3733', '₹25952', '₹4182', '₹7692', '₹12473', nan, '₹3284', '₹6371', '₹3971', '₹4549', '₹3487', nan, '₹5882', nan, '₹4676', '₹3070', '₹2879', nan, '₹4615', '₹6667', '₹5396', '₹4533', '₹4813', '₹6371', '₹6500', '₹3788', '₹5911', '₹3507', '₹3200', '₹5639', '₹5913', '₹4525', '₹6366', '₹3750', nan, '₹3491', '₹2577', '₹4968', '₹3426', '₹6190', '₹2327', '₹3372', '₹3368', '₹3333', '₹4000', '₹1540', '₹2706', '₹3099', '₹2772', '₹3000', '₹3467', '₹2538', '₹2913', '₹1852', '₹7479', '₹3984', '₹3109', '₹2327', '₹3143', '₹3600', '₹2778', '₹3600', '₹2860', '₹2326', '₹2273', '₹2759', '₹2833', '₹2753', '₹5440', '₹3097', '₹4500000', '₹6190', '₹3589', '₹2481', '₹4000', '₹3000', '₹4695', '₹3400', '₹2511', '₹3167', '₹2500', '₹3582', '₹3997', '₹2555', '₹3200', '₹3359', '₹2627', '₹3611', '₹3328', '₹3045', '₹2264', '₹3745', '₹4561', '₹2592', '₹3944', '₹6000', '₹4077', '₹1846', '₹1941', '₹2167', '₹2917', '₹2118', '₹2244', '₹2077', '₹1746', '₹2585', '₹2587', '₹4242', '₹1840', '₹1500', '₹2393', '₹1611', '₹1900', '₹2348', '₹2790', '₹1503', '₹2211', '₹2400', '₹1913', '₹2367', '₹2667', '₹3711', '₹3285', '₹1818', '₹2857', '₹3797', '₹3509', '₹4110', '₹7556', '₹6611', nan, '₹4296', '₹5857', '₹4940', '₹4762', '₹6071', '₹5115', '₹4208', '₹6586', '₹4375', '₹3863', '₹4000', nan, nan, '₹4462', '₹5000', '₹5103', '₹6591', '₹5743', '₹14818', '₹4848', '₹6389', '₹5000', nan, '₹6667', '₹6457']
['Floor17 out of 22', 'Floor7 out of 9', 'Floor8 out of 25', 'Floor1 out of 2', 'Floor8 out of 16', 'Floor9 out of 14', 'Floor18 out of 32', 'Floor14 out of 26', 'Floor1 out of 4', 'Floor25 out of 25', 'Floor16 out of 24', 'Floor11 out of 22', 'Floor6 out of 34', 'Floor3 out of 6', 'Floor5 out of 15', 'Floor11 out of 20', 'Floor6 out of 23', 'Floor19 out of 20', 'Floor12 out of 24', 'Floor26 out of 27', 'Floor17 out of 19', 'Floor1 out of 4', 'Floor10 out of 19', 'Floor1 out of 5', 'Floor20 out of 21', 'Floor6 out of 14', 'Floor4 out of 22', 'Floor7 out of 21', 'Express Park View 2', 'Floor25 out of 25', 'Floor22 out of 25', 'Floor3 out of 3', 'Floor10 out of 14', 'Floor3 out of 10', 'SocietyKendriya Vihar Sector 56', 'Floor1 out of 4', 'Floor7 out of 10', 'Floor19 out of 20', 'Floor9 out of 15', 'Floor3 out of 14', 'Monsoon Breeze Phase 2', 'Floor10 out of 27', 'Floor8 out of 22', 'Floor4 out of 4', 'Floor3 out of 4', 'Floor4 out of 9', 'Floor5 out of 6', 'Floor2 out of 3', 'Floor1 out of 4', 'Floor8 out of 10', 'FloorGround out of 4', 'Floor1 out of 18', 'Floor2 out of 14', 'Floor4 out of 9', 'Floor2 out of 3', 'City Plot Phase 1', 'Floor3 out of 4', 'Floor3 out of 4', 'Floor2 out of 4', 'FloorGround out of 32', 'Floor1 out of 3', 'Floor6 out of 12', 'Floor19 out of 47', 'Floor11 out of 14', 'Floor2 out of 2', 'Floor4 out of 5', 'Floor4 out of 8', 'Floor3 out of 4', 'Floor6 out of 14', 'Floor7 out of 25', 'Floor22 out of 23', 'Floor5 out of 5', 'Floor2 out of 4', 'Floor1 out of 4', 'Floor12 out of 14', 'Floor5 out of 6', 'Floor14 out of 23', 'Floor7 out of 13', 'Floor9 out of 9', 'Floor7 out of 19', 'Floor4 out of 5', 'Floor3 out of 4', 'Floor5 out of 5', 'Floor2 out of 5', 'Floor4 out of 5', 'Floor4 out of 4', 'Floor1 out of 16', 'Floor1 out of 4', 'Floor3 out of 5', 'Floor3 out of 5', 'Floor5 out of 5', 'Floor4 out of 4', 'Floor4 out of 4', 'Floor2 out of 5', 'Floor6 out of 6', 'Floor4 out of 6', 'Floor5 out of 5', 'Basement out of 4', 'Floor5 out of 8', 'Floor3 out of 10', 'FloorGround out of 3', 'Floor1 out of 4', 'Floor1 out of 3', 'Floor2 out of 6', 'Floor3 out of 8', 'Floor6 out of 6', 'Floor10 out of 10', 'Floor7 out of 10', 'Floor3 out of 6', 'Floor3 out of 5', 'Floor7 out of 11', 'Floor4 out of 7', 'Floor4 out of 8', 'Floor5 out of 11', 'FloorGround out of 4', 'Floor1 out of 1', 'Floor2 out of 10', 'Floor2 out of 4', 'Floor3 out of 6', 'Floor5 out of 10', 'Floor4 out of 6', 'Floor2 out of 3', 'Floor1 out of 6', 'Floor3 out of 8', 'Floor3 out of 4', 'Floor2 out of 6', 'Floor2 out of 5', 'Floor1 out of 2', 'Floor2 out of 5', 'Floor8 out of 9', 'Floor1 out of 10', 'Floor2 out of 2', 'Floor1 out of 2', 'Floor1 out of 3', 'Floor9 out of 11', 'FloorGround out of 3', 'Floor3 out of 5', 'Floor7 out of 10', 'Floor4 out of 14', 'Floor5 out of 9', 'Floor3 out of 13', 'Floor1 out of 11', 'Floor2 out of 11', 'Floor7 out of 15', 'Floor1 out of 10', 'Floor3 out of 4', 'Floor11 out of 14', 'Floor2 out of 2', 'Floor12 out of 12', 'Floor3 out of 9', 'Floor9 out of 9', 'Floor2 out of 11', 'Floor2 out of 6', 'Floor6 out of 13', 'Floor5 out of 6', 'Floor7 out of 10', 'Floor3 out of 13', 'Floor3 out of 13', 'Floor1 out of 9', 'Floor4 out of 14', 'Floor13 out of 15', 'Basement out of 15', 'Floor9 out of 16', 'Floor2 out of 14', 'Floor2 out of 4', 'Floor8 out of 13', 'Floor15 out of 15', 'Floor5 out of 7', 'Floor4 out of 9', 'Floor9 out of 14', 'Floor13 out of 14', 'Floor11 out of 16', 'Floor11 out of 15', 'SocietyAvalon Residency Phase 2', 'Floor12 out of 15', 'Floor9 out of 15', 'Floor9 out of 12', 'Floor7 out of 14', 'Floor14 out of 14', 'Floor4 out of 9', 'Floor2 out of 5', 'Floor9 out of 14', 'Floor2 out of 13', 'SocietyKrish City Phase 2', 'Floor2 out of 4', 'Floor6 out of 8', 'Floor3 out of 8', 'Floor2 out of 2', 'Floor1 out of 4', 'Floor4 out of 4', 'Floor4 out of 5', 'Floor8 out of 8', 'Floor2 out of 3', 'Floor2 out of 4', 'Floor3 out of 4', 'Basement out of 1', 'Floor1 out of 4', 'Floor3 out of 4', 'Floor1 out of 5', 'Floor2 out of 4', 'Floor3 out of 6', 'FloorGround out of 10', 'Floor3 out of 4', 'Floor7 out of 7', 'Floor4 out of 7', 'FloorGround out of 3', 'Floor1 out of 3', 'FloorGround out of 2', 'FloorGround out of 5', 'Floor1 out of 3', 'Floor3 out of 4', 'Floor3 out of 3', 'FloorGround out of 4']
210
210
210
210
210
210
210
213


# inserting NaN's at index positions
Owner.append(np.nan)


# inserting NaN's at index positions
Carpet_Area.insert(8,np.nan)
Carpet_Area.insert(45,np.nan)
Carpet_Area.insert(67,np.nan)
Carpet_Area.insert(124,np.nan)
Carpet_Area.insert(130,np.nan)
Carpet_Area.insert(137,np.nan)


# inserting NaN's at index positions
per_sqt.insert(5,np.nan)
per_sqt.insert(2,np.nan)
per_sqt.insert(32,np.nan)
per_sqt.insert(59,np.nan)
per_sqt.insert(65,np.nan)
per_sqt.insert(67,np.nan)
per_sqt.insert(71,np.nan)
per_sqt.insert(88,np.nan)
per_sqt.insert(184,np.nan)
per_sqt.insert(196,np.nan)
per_sqt.insert(197,np.nan)
per_sqt.insert(207,np.nan)


# Creation the Dict with cloumns names as strings and values as column names in which values are present.
df3 = {'City':City,
             'Area':Area,
             'Owner':Owner,
             'BHK':BHK,
             'Floor':Floor[0:210],
             'price':price,
             'Carpet_Area':Carpet_Area,
             'per_sqt':per_sqt
}


# creation of DataFrame
Project3=pd.DataFrame(df3)


Project3


# conversion of DataFrame to csv
Project3.to_csv('DF3.csv')


# DataFrame concatenation into the final DF
FINALPROJECT = pd.concat([Project1,Project2,Project3])


# Resetting the index numbers after formaton of the finam DF
FINALPROJECT = FINALPROJECT.reset_index()


# Final uncleaned DataFrame
FINALPROJECT


# final scraped data ---> uncleaned
FINALPROJECT.to_csv('Magicbrics_Project.csv')


# import libraries
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style('whitegrid')
plt.style.use('bmh')

import warnings
warnings.filterwarnings('ignore')

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'

# for HD visualizations
%config InlineBackend.figure_format='retina'


# load the Dataset
dff = pd.read_csv(r'C:\Users\GUDLA RAGUWING\Data Science Course\MagicBrics_Webscraping_project\Magicbrics_Project.csv')


dff.head(20)


# Droping the unwnated columns
dff.drop(['Unnamed: 0','index'], axis = 1, inplace = True)


dff.head()


dff.duplicated()
dff.duplicated().value_counts()

0      False
1      False
2      False
3      False
4      False
       ...  
415    False
416    False
417    False
418    False
419    False
Length: 420, dtype: bool

False    420
dtype: int64


dff.isna()


dff.isna().sum()

City            0
Area            0
Owner           1
BHK             0
Floor           3
price           0
Carpet_Area     8
per_sqt        29
dtype: int64


dff[dff.per_sqt.isna()]


dff[dff.Carpet_Area.isna()]


dff[dff.Floor.isna()]


dff[dff.Owner.isna()]


dff.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   City         420 non-null    object 
 1   Area         420 non-null    object 
 2   Owner        419 non-null    object 
 3   BHK          420 non-null    object 
 4   Floor        417 non-null    object 
 5   price        420 non-null    object 
 6   Carpet_Area  412 non-null    float64
 7   per_sqt      391 non-null    object 
dtypes: float64(1), object(7)
memory usage: 26.4+ KB


dff[['BHK','price','Carpet_Area','per_sqt']].iloc[40:50]


dff.per_sqt=dff.per_sqt.str.replace('₹', '')
dff.price= dff.price.str.replace('₹','')
dff.price = dff.price.str.replace('Cr','00000')
dff.price = dff.price.str.replace('Lac','00000')
dff.price=dff.price.str.replace('.','')
dff.price=dff.price.str.replace(' ','')
dff.Floor=dff.Floor.str.replace('Floor','')
dff.BHK=dff.BHK.str.replace(' ','3')


dff.rename(columns = {'price':'price_in_crores', 'per_sqt':'per_sqt_rupees'}, inplace = True)


dff.price_in_crores=dff.price_in_crores.astype(int)


dff.head(50)


np.round(dff.isna().sum()/len(dff)*100, 2).astype(str) + '%'

City                0.0%
Area                0.0%
Owner              0.24%
BHK                 0.0%
Floor              0.71%
price_in_crores     0.0%
Carpet_Area         1.9%
per_sqt_rupees      6.9%
dtype: object


dff.BHK.isna().sum()

0


dff.BHK = dff.BHK.astype(str)


sns.boxplot(data=dff.BHK);


dff.Area.iloc[[47,88,102,120,122,144,196,205,280,289]]
# Replace these values with NaN's and then fill them with mode of the column
droplist1=[47,88,102,120,122,144,196,205,280,289]

dff.Area= dff.Area.drop(droplist1,axis=0)

dff.Area.iloc[102]

47          K
88         Gu
102      Poth
120         H
122         S
144         H
196     Bill 
205     Bill 
280         K
289      Jepp
Name: Area, dtype: object

nan


dff = dff.replace(r'^\s*$', np.nan, regex=True)
print(dff.head(20))

         City                   Area                 Owner BHK  \
0   bangalore         Kattigenahalli          Omkar Pandey   3   
1   bangalore          Ayodaya Nagar                 gokul   2   
2   bangalore       Electronic City           Thayumanavan   2   
3   bangalore           Tumkur Road               Prasanna   3   
4   bangalore            Kudlu Gate            Akash Akash   2   
5   bangalore            Hal Stage 2               av nath   3   
6   bangalore          Murugeshpalya       Sangeeta Pillai   2   
7   bangalore            Begur Road                ganga k   3   
8   bangalore        Judicial Layout               Anjan K   3   
9   bangalore              Bellandur           Omesh Saraf   2   
10  bangalore         Kasturi Nagar          Mohd Hussain    3   
11  bangalore   Koramangala Block 1             Rahul Jain   3   
12  bangalore         Sarjapur Road    Gaurav Kumar GAURAV   3   
13  bangalore           Devanahalli                   Ancy   2   
14  bangalore       Hanumantha Nagar                 Anoop   2   
15  bangalore                    NaN                geetha   2   
16  bangalore         Doddakannelli            Omkar Omkar   2   
17  bangalore       Electronic City                ruchika   2   
18  bangalore         Sarjapur Road               jeswanth   3   
19  bangalore                Jigani           vikas saxena   3   

                 Floor  price_in_crores  Carpet_Area per_sqt_rupees  
0      Ground out of 4          6000000      1000.00           4950  
1      Ground out of 4          8000000      1150.00           6957  
2           4 out of 4          4800000      1060.00           4528  
3         14 out of 15         13500000      1790.00           7542  
4           2 out of 4          6100000       803.00           5706  
5           1 out of 4         13000000      1640.00           7303  
6           3 out of 5          5000000      1250.00           5000  
7   Basement out of 17         18000000      1588.00          11335  
8          3 out of 14           100000      1000.00           8889  
9          2 out of 10         10200000      1465.00           6997  
10     Ground out of 7         14500000      1500.00           8146  
11        16 out of 16         38500000      1418.00          20632  
12     Ground out of 5          7000000      1350.00           5655  
13          1 out of 7          8500000      1000.00           6071  
14          2 out of 4          9800000      1250.00           9800  
15          2 out of 5          6000000       857.00           6367  
16          3 out of 6          7800000       753.00           5158  
17          1 out of 7          5700000      1186.00           6374  
18          1 out of 4         10800000      1250.00           4553  
19         6 out of 12          5700000       770.00           4545


# filling Area column with mode of the column
dff.Area.value_counts()
dff.Area.mode()
dff.Area.mode().values
Ar_mode=dff.Area.mode().values[0]
dff.Area.fillna(Ar_mode, inplace=True)
dff.Area.isna().sum()

 Alwar Bypass Road      22
 Greater Noida West      7
 Jwalapur                6
 Sikandra                5
 Dayal Bagh              5
                        ..
 Sector 2 Masibari       1
 Upper Chutia            1
 Hazaribag Road          1
 Lalpur                  1
 Sriramapura             1
Name: Area, Length: 261, dtype: int64

0     Alwar Bypass Road 
Name: Area, dtype: object

array([' Alwar Bypass Road '], dtype=object)

0


# filling Owner column with mode of the column
dff.Owner.value_counts()
dff.Owner.mode()
dff.Owner.mode().values
Ow_mode=dff.Owner.mode().values[0]
dff.Owner.fillna(Ow_mode, inplace=True)
dff.Owner.isna().sum()

 Prasanna         2
 Sunil Kumar      2
 vijay            2
 Srikanth         2
 sanjeev          2
                 ..
 Mukesh Mukesh    1
 Manit Kumar      1
 Neeraj           1
 Abhishek         1
 Chakko           1
Name: Owner, Length: 414, dtype: int64

0        Prasanna
1        Srikanth
2     Sunil Kumar
3         sanjeev
4           vijay
Name: Owner, dtype: object

array([' Prasanna', ' Srikanth', ' Sunil Kumar', ' sanjeev', ' vijay'],
      dtype=object)

0


dff[dff.Owner.isna()]


# filling Floor column with mode of the column
dff.Floor.value_counts()
dff.Floor.mode()
dff.Floor.mode().values
FL_mode=dff.Floor.mode().values[0]
dff.Floor.fillna(FL_mode, inplace=True)
dff.Floor.isna().sum()

3 out of 4          24
1 out of 4          22
2 out of 4          20
4 out of 4          16
2 out of 5          14
                    ..
17 out of 19         1
26 out of 27         1
12 out of 24         1
Ground out of 76     1
Ground out of 10     1
Name: Floor, Length: 145, dtype: int64

0    3 out of 4
Name: Floor, dtype: object

array(['3 out of 4'], dtype=object)

0


dff[dff.Floor.isna()]


dff.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   City             420 non-null    object 
 1   Area             420 non-null    object 
 2   Owner            420 non-null    object 
 3   BHK              420 non-null    object 
 4   Floor            420 non-null    object 
 5   price_in_crores  420 non-null    int32  
 6   Carpet_Area      412 non-null    float64
 7   per_sqt_rupees   391 non-null    object 
dtypes: float64(1), int32(1), object(6)
memory usage: 24.7+ KB


dff.per_sqt_rupees.skew()
# As you can observe my per_sqt skewness in extremely high that represents that huge outliers are present.
# Or intensional put there.

19.756402504713776


# creating the droplist
droplist=[30,31,49,34,37,41,51,334]


# droping the droplist
dff.per_sqt_rupees= dff.per_sqt_rupees.drop(droplist,axis=0)

dff.per_sqt_rupees.iloc[334]

nan


dff.per_sqt_rupees=dff.per_sqt_rupees.astype(float)


# calculatin the median using pandas and filling using fillna()
per_median=dff.per_sqt_rupees.median()
dff.per_sqt_rupees.fillna(per_median, inplace=True)


# type casting
dff.per_sqt_rupees=dff.per_sqt_rupees.astype(int)


dff[dff.per_sqt_rupees.isna()]


dff.per_sqt_rupees.skew()
# As you can see that the skewness is significantly reduced

3.503912518035357


import scipy.stats as stats


Q1, Q2, Q3 = tuple(dff.per_sqt_rupees.quantile(q = [0.25,0.5,0.75]).values)

print(Q1, Q2, Q3)

IQR = Q3-Q1
UL = Q3 + 1.5*IQR
LL = Q1-1.5*IQR

print(IQR, UL, LL)

3295.0 4274.0 5481.25
2186.25 8760.625 15.625


dff[(dff.per_sqt_rupees > UL) | (dff.per_sqt_rupees < LL)]


# Suppress Scientific Notation in Numpy
np.set_printoptions(suppress=True)

# Suppress Scientific Notation in Pandas
pd.set_option('display.float_format', '{:.2f}'.format)


zscore_array = stats.zscore(dff.per_sqt_rupees)


np.where(zscore_array>2,1,0).sum()
np.where(zscore_array<-2,1,0).sum()

15

0


dff[zscore_array>2]


# Extreme values/Outliers cause variance

dff.per_sqt_rupees.var()
dff.per_sqt_rupees.std()

12627149.27491759

3553.4700329280377


from sklearn.preprocessing import StandardScaler, MinMaxScaler


# Normalization
mm1 = MinMaxScaler()


dff1 = mm1.fit_transform(dff.per_sqt_rupees.values.reshape(-1,1))


dff1.min()
dff1.max()

0.0

1.0


sns.histplot(dff1, kde = True);


sns.boxplot(dff1);


# Standardization

ss1 = StandardScaler()


dff2 = ss1.fit_transform(dff.per_sqt_rupees.values.reshape(-1,1))


round(dff2.mean())
round(dff2.std())

0

1


sns.histplot(dff2, kde = True);


sns.boxplot(dff2);


np.log(dff.per_sqt_rupees)

0     8.51
1     8.85
2     8.42
3     8.93
4     8.65
      ... 
415   8.76
416   8.52
417   8.36
418   8.80
419   8.77
Name: per_sqt_rupees, Length: 420, dtype: float64


sns.histplot(np.log(dff.per_sqt_rupees), kde = True);


sns.boxplot(np.log(dff.per_sqt_rupees));


# Log transformation seems to be fitting well for this data

# compare the variance
dff.per_sqt_rupees.var()

np.log(dff.per_sqt_rupees).var()

12627149.27491759

0.23814150733330514


dff.per_sqt_rupees**(1/3)

0     17.04
1     19.09
2     16.54
3     19.61
4     17.87
       ... 
415   18.56
416   17.10
417   16.23
418   18.82
419   18.62
Name: per_sqt_rupees, Length: 420, dtype: float64


sns.boxplot(dff.per_sqt_rupees**(1/3));


stats.boxcox(dff.per_sqt_rupees)

len(stats.boxcox(dff.per_sqt_rupees))

type(stats.boxcox(dff.per_sqt_rupees))

x,y = stats.boxcox(dff.per_sqt_rupees)

(array([2.11195827, 2.1180167 , 2.11020754, 2.11931871, 2.11460509,
        2.11880526, 2.11215123, 2.12519303, 2.12182256, 2.11811077,
        2.12051637, 2.13204143, 2.11444302, 2.11570617, 2.12322127,
        2.11653027, 2.11274285, 2.11654908, 2.11031783, 2.11028263,
        2.11923022, 2.12644576, 2.11965543, 2.11326993, 2.11909397,
        2.11336791, 2.11905534, 2.12376861, 2.11073751, 2.13437529,
        2.109034  , 2.109034  , 2.109034  , 2.13334783, 2.109034  ,
        2.13459321, 2.13095717, 2.109034  , 2.11233005, 2.109034  ,
        2.12541444, 2.109034  , 2.11867025, 2.109034  , 2.12350305,
        2.12830742, 2.11351691, 2.12597449, 2.12754181, 2.109034  ,
        2.13237023, 2.109034  , 2.13307212, 2.12442144, 2.13397708,
        2.1131393 , 2.1214341 , 2.11215123, 2.10983033, 2.12191778,
        2.109034  , 2.1117427 , 2.11582094, 2.1131393 , 2.11462402,
        2.12092258, 2.12099264, 2.109034  , 2.11247659, 2.12222823,
        2.11582379, 2.12010539, 2.10932499, 2.12551196, 2.10796302,
        2.10764248, 2.11940649, 2.11323123, 2.11683285, 2.11606681,
        2.10936271, 2.11706628, 2.12573432, 2.11307169, 2.11754896,
        2.12023905, 2.10295901, 2.11268783, 2.11058757, 2.10764779,
        2.11080974, 2.10832227, 2.10810265, 2.11951005, 2.10547099,
        2.10147698, 2.11923022, 2.11215123, 2.10110878, 2.10576335,
        2.10625589, 2.10794222, 2.10343587, 2.11302164, 2.10826132,
        2.10301671, 2.11565705, 2.11279764, 2.10874669, 2.11118171,
        2.10596111, 2.09751594, 2.11080974, 2.10878109, 2.11832878,
        2.109034  , 2.109034  , 2.109034  , 2.109034  , 2.109034  ,
        2.11075879, 2.10867271, 2.11333999, 2.11392701, 2.10962855,
        2.10614456, 2.10639534, 2.11022082, 2.10923975, 2.10615631,
        2.10585356, 2.109034  , 2.10025353, 2.10757324, 2.11385679,
        2.11292096, 2.11333999, 2.09859975, 2.11064774, 2.10822054,
        2.10785347, 2.109034  , 2.11412221, 2.10438151, 2.10677224,
        2.109034  , 2.11001124, 2.1127867 , 2.11136186, 2.12086111,
        2.09671989, 2.11021197, 2.10028757, 2.11315703, 2.09290595,
        2.11369819, 2.10220465, 2.10078182, 2.10461086, 2.10240779,
        2.09756577, 2.10744993, 2.10557518, 2.10472105, 2.10747145,
        2.11022082, 2.10199894, 2.10680603, 2.09990929, 2.09213461,
        2.10913486, 2.10209069, 2.10287208, 2.109034  , 2.10588352,
        2.10576335, 2.09445579, 2.10605607, 2.10668174, 2.10055745,
        2.10072394, 2.11336791, 2.09588694, 2.10696828, 2.10062424,
        2.10327378, 2.09103735, 2.10086417, 2.10846348, 2.09159365,
        2.09692741, 2.109034  , 2.109034  , 2.10251956, 2.10585956,
        2.10335856, 2.08525251, 2.09702001, 2.09956679, 2.109034  ,
        2.09267388, 2.10664198, 2.11412221, 2.10277006, 2.109034  ,
        2.09670945, 2.10764779, 2.10110878, 2.09695833, 2.11058757,
        2.10742299, 2.10327378, 2.109034  , 2.10607381, 2.09621186,
        2.11347205, 2.109034  , 2.10882029, 2.10812323, 2.11138218,
        2.10586555, 2.11696829, 2.11699158, 2.12200562, 2.10717273,
        2.11128027, 2.11079278, 2.11300014, 2.11332951, 2.11328047,
        2.11065203, 2.11222762, 2.09572229, 2.11094886, 2.09505992,
        2.10734185, 2.10735811, 2.10940033, 2.10078182, 2.10557518,
        2.12182256, 2.12358736, 2.109034  , 2.11323123, 2.10690695,
        2.10980755, 2.11215123, 2.11173874, 2.12616486, 2.12297505,
        2.12023905, 2.11268783, 2.11852906, 2.12581117, 2.12049573,
        2.12064121, 2.10972522, 2.11468377, 2.12797043, 2.11689035,
        2.11923022, 2.12267558, 2.11837462, 2.1250239 , 2.10615631,
        2.13419956, 2.10858342, 2.11962895, 2.12641869, 2.109034  ,
        2.10325961, 2.11654102, 2.10749294, 2.11030024, 2.10463686,
        2.109034  , 2.11514851, 2.109034  , 2.11084783, 2.10166587,
        2.10009949, 2.109034  , 2.11058757, 2.11731016, 2.11358562,
        2.11022967, 2.11141462, 2.11654102, 2.11688252, 2.10647609,
        2.11523577, 2.10476619, 2.10265266, 2.11439173, 2.11524176,
        2.11019424, 2.11652758, 2.10625589, 2.109034  , 2.10466281,
        2.09728488, 2.11202806, 2.10423559, 2.11604441, 2.09456176,
        2.10387154, 2.10384424, 2.10360326, 2.10764779, 2.08212959,
        2.09854336, 2.10189124, 2.09915379, 2.10110878, 2.10450644,
        2.0968861 , 2.10038928, 2.08798098, 2.1191857 , 2.10756256,
        2.10196824, 2.09456176, 2.10222735, 2.10535369, 2.09920822,
        2.10535369, 2.09993535, 2.09455002, 2.09391674, 2.09903524,
        2.09969934, 2.09898026, 2.11373549, 2.1018758 , 2.109034  ,
        2.11604441, 2.10528537, 2.09628684, 2.10764779, 2.10110878,
        2.11092788, 2.10406137, 2.09660472, 2.10240779, 2.09648881,
        2.10524173, 2.10763185, 2.09706102, 2.10265266, 2.10378262,
        2.09778339, 2.10542171, 2.10356854, 2.10146906, 2.09380705,
        2.10622667, 2.11035294, 2.09743591, 2.10734727, 2.1154997 ,
        2.1080511 , 2.08788233, 2.08939143, 2.09258287, 2.10042305,
        2.0919335 , 2.093561  , 2.09137302, 2.08616579, 2.09736559,
        2.09738571, 2.1088789 , 2.08778321, 2.08125326, 2.09532097,
        2.0836058 , 2.0887537 , 2.09480672, 2.09931658, 2.08132016,
        2.09314791, 2.0953997 , 2.08895807, 2.09502559, 2.09817231,
        2.10602646, 2.1032667 , 2.08741567, 2.09990929, 2.10652777,
        2.10477907, 2.10822054, 2.11934805, 2.11716853, 2.109034  ,
        2.10913964, 2.11507278, 2.11191934, 2.11120642, 2.11570617,
        2.11258449, 2.10871222, 2.11710473, 2.10951258, 2.10690136,
        2.10764779, 2.109034  , 2.109034  , 2.10991203, 2.11215123,
        2.11253995, 2.11711752, 2.11472136, 2.12849299, 2.11155565,
        2.11658928, 2.11215123, 2.109034  , 2.11731016, 2.1167698 ]),
 -0.4643823843215321)

2

tuple


sns.boxplot(x);


drop_list = [239,259]
dff.Carpet_Area=dff.Carpet_Area.drop(drop_list, axis=0)


Car_median=dff.Carpet_Area.median()
dff.Carpet_Area.fillna(Car_median, inplace=True)
dff.Carpet_Area.isna().sum()

0


dff[dff.Carpet_Area.isna()]


Q1, Q2, Q3 = tuple(dff.Carpet_Area.quantile(q = [0.25,0.5,0.75]).values)


print(Q1, Q2, Q3)

812.0 1052.5 1400.0


IQR = Q3-Q1
UL = Q3 + 1.5*IQR
LL = Q1-1.5*IQR

print(IQR, UL, LL)

588.0 2282.0 -70.0


dff[(dff.Carpet_Area > UL) | (dff.Carpet_Area < LL)]


# Suppress Scientific Notation in Numpy
np.set_printoptions(suppress=True)

# Suppress Scientific Notation in Pandas
pd.set_option('display.float_format', '{:.2f}'.format)


zscore_array = stats.zscore(dff.Carpet_Area)


np.where(zscore_array>2,1,0).sum()
np.where(zscore_array<-2,1,0).sum()

15

2


zscore_array>2

0      False
1      False
2      False
3      False
4      False
       ...  
415    False
416    False
417    False
418    False
419    False
Name: Carpet_Area, Length: 420, dtype: bool


dff[zscore_array>2]


dff.Carpet_Area.skew()

1.672304347952084


sns.boxplot(data=dff.Carpet_Area);


# Extreme values/Outliers cause variance

dff.Carpet_Area.var()
dff.Carpet_Area.std()

281486.8008523696

530.5532969008576


mm = MinMaxScaler()


dff_ = mm.fit_transform(dff.Carpet_Area.values.reshape(-1,1))


dff_.min()
dff_.max()

0.0

1.0


sns.histplot(dff_, kde = True);


sns.boxplot(dff_);


# Standardization

ss = StandardScaler()

dff_s = ss.fit_transform(dff.Carpet_Area.values.reshape(-1,1))

round(dff_s.mean())
dff_s.std()

sns.histplot(dff_s, kde = True);


sns.boxplot(dff_s);


# log transformation method
np.log(dff.Carpet_Area)

0     6.91
1     7.05
2     6.97
3     7.49
4     6.69
      ... 
415   6.98
416   6.98
417   7.09
418   6.80
419   7.02
Name: Carpet_Area, Length: 420, dtype: float64


sns.histplot(np.log(dff.Carpet_Area), kde = True);


sns.boxplot(np.log(dff.Carpet_Area));


# cube root transformation  method
dff.Carpet_Area**(1/3)

0     10.00
1     10.48
2     10.20
3     12.14
4      9.29
       ... 
415   10.23
416   10.26
417   10.63
418    9.65
419   10.37
Name: Carpet_Area, Length: 420, dtype: float64


sns.boxplot(dff.Carpet_Area**(1/3));


(dff.Carpet_Area**(1/3)).skew()
dff.Carpet_Area.skew()

-0.04396947259226398

1.672304347952084


import scipy.stats as stats


stats.boxcox(dff.Carpet_Area)

len(stats.boxcox(dff.Carpet_Area))

type(stats.boxcox(dff.Carpet_Area))

x,y = stats.boxcox(dff.Carpet_Area)

(array([36.60342465, 38.83608626, 37.51921819, 46.7795446 , 33.33918254,
        45.09564664, 40.22831393, 44.49029921, 36.60342465, 43.00915977,
        43.43801727, 42.42343321, 41.55489717, 36.60342465, 40.22831393,
        34.27800037, 32.43533305, 39.34539935, 40.22831393, 32.7465937 ,
        50.50075042, 47.96099206, 42.94739202, 47.80235806, 48.48438448,
        40.22831393, 47.10600059, 49.60803085, 32.19443999, 22.70129238,
        22.0533256 , 29.63094782, 38.97851499, 30.75218253, 37.39895004,
        31.14598915, 24.6706847 , 22.73152132, 32.85546022, 33.28592469,
        25.97259129, 25.46284768, 30.94996522, 47.64296116, 33.63873449,
        34.15835321, 39.14845575, 24.77833042, 28.88535848, 27.35439424,
        28.51332272, 31.04819443, 38.54897227, 28.6014687 , 27.99893593,
        29.20815607, 33.14331244, 35.57311056, 30.086475  , 37.27812921,
        33.28592469, 39.54094628, 41.55489717, 35.81461641, 36.61895551,
        35.00036151, 33.28592469, 34.19259854, 43.36493058, 31.4368166 ,
        30.12740941, 42.13253116, 35.49208998, 26.3213334 , 28.9935158 ,
        27.40113425, 36.4475984 , 25.84645722, 26.44434466, 32.89163623,
        34.15835321, 28.1118766 , 39.45730979, 34.96723052, 31.4368166 ,
        29.42061407, 32.60063978, 37.1415367 , 33.28592469, 40.89929792,
        33.69120681, 45.77991497, 44.33710093, 31.4368166 , 43.92081655,
        38.83608626, 41.03161834, 36.60342465, 35.2474408 , 40.89929792,
        29.42061407, 36.60342465, 40.89929792, 43.63205749, 46.88872727,
        38.11249792, 44.86420952, 44.63104332, 36.13307467, 38.59223361,
        35.41080635, 47.69617883, 37.36879691, 45.09564664, 33.98639365,
        33.28592469, 35.00036151, 30.45211719, 33.28592469, 37.40648292,
        38.25877797, 43.43801727, 38.83608626, 28.84193681, 44.15938336,
        49.10188439, 35.49208998, 40.63280415, 40.22831393, 43.43801727,
        42.00515007, 39.70746606, 36.60342465, 33.28592469, 48.22371281,
        40.9787635 , 43.92081655, 39.26116752, 36.14889322, 35.81461641,
        38.31706749, 37.41401364, 51.46662099, 35.81461641, 38.83608626,
        45.21072735, 41.39890662, 46.44977306, 36.10140804, 43.43801727,
        43.31610776, 32.37996627, 38.11249792, 30.45211719, 57.99999124,
        39.67978201, 31.79985456, 42.82347282, 43.24272515, 37.74325448,
        42.04342245, 36.98892473, 34.83425981, 37.36879691, 27.18992309,
        27.88535708, 30.45211719, 31.4368166 , 30.45211719, 44.63104332,
        33.28592469, 34.44790722, 42.82347282, 35.00036151, 46.88872727,
        29.50499932, 35.2474408 , 45.77991497, 32.37996627, 49.32551329,
        43.43801727, 35.00036151, 32.92775622, 39.65207036, 31.4368166 ,
        32.47217039, 43.61996625, 47.96099206, 43.00915977, 37.8175141 ,
        25.97259129, 36.75831424, 34.15835321, 36.85080459, 36.98892473,
        33.28592469, 36.29082027, 33.28592469, 30.24974754, 38.83608626,
        52.48111879, 35.00036151, 52.40694376, 32.37996627, 44.39611324,
        18.12845607, 29.42061407, 45.21072735, 54.65683745, 37.40648292,
        39.54094628, 38.40426565, 36.52562955, 36.13307467, 34.91745052,
        46.61507887, 52.53665056, 30.6526184 , 37.40648292, 35.81461641,
        36.52562955, 59.64802508, 61.38742663, 50.98699463, 35.16535421,
        33.98639365, 43.80083235, 34.36310234, 37.444115  , 29.73533555,
        38.90739356, 35.81461641, 33.98639365, 34.49864868, 42.13253116,
        38.33162015, 40.69966161, 43.36493058, 38.33162015, 37.40648292,
        35.00036151, 46.76860612, 43.43801727, 32.37996627, 22.0533256 ,
        46.3390968 , 28.77663264, 45.29103387, 63.6983569 , 46.88872727,
        40.89929792, 35.00036151, 48.48438448, 44.11181816, 39.7765559 ,
        37.40648292, 29.63094782, 46.28361566, 53.50401875, 37.40648292,
        42.82347282, 40.22831393, 44.04033194, 46.88872727, 28.33588099,
        62.90146809, 35.97434553, 43.43801727, 43.43801727, 58.5489484 ,
        30.85129634, 38.11249792, 45.77991497, 40.56578906, 41.29444382,
        35.09948895, 58.23959371, 37.40648292, 42.06890968, 40.36378797,
        30.6526184 , 39.06361718, 64.64538183, 36.2120694 , 42.06890968,
        37.66878634, 47.64296116, 37.21750943, 36.60342465, 41.16333119,
        32.98183167, 11.24291181, 36.60342465, 41.29444382, 41.63257905,
        34.73405816, 44.78279979, 38.11249792, 41.43798308, 39.54094628,
        36.13307467, 39.54094628, 48.84596197, 33.98639365, 40.22831393,
        35.70220197, 33.26814532, 35.00036151, 37.36879691, 26.88075378,
        31.4368166 , 33.55102281, 27.30754293, 34.43096987, 31.83773104,
        39.54094628, 37.06534237, 43.43801727, 46.9431815 , 39.95539237,
        36.05383406, 40.22831393, 41.3858694 , 35.97434553, 39.54094628,
        36.60342465, 43.43801727, 34.32909736, 28.33588099, 35.09948895,
        35.00036151, 43.92081655, 40.22831393, 34.15835321, 37.40648292,
        37.36879691, 34.12405926, 41.55489717, 37.36879691, 42.19601531,
        37.40648292, 39.42937466, 38.33162015, 36.60342465, 30.6526184 ,
        45.49660166, 52.95959843, 37.40648292, 34.36310234, 40.22831393,
        42.19601531, 46.88872727, 46.3390968 , 45.49660166, 35.97434553,
        35.65387035, 42.51140332, 35.42708423, 34.3461058 , 43.43801727,
        39.04945184, 28.44696293, 36.29082027, 26.37063334, 40.01018955,
        32.37996627, 32.92775622, 30.45211719, 30.04546242, 29.42061407,
        42.57407973, 30.6526184 , 33.55102281, 40.22831393, 39.12020538,
        38.27336221, 36.60342465, 38.83608626, 33.79580655, 31.4368166 ,
        35.81461641, 40.22831393, 38.83608626, 29.3994648 , 32.37996627,
        44.51380098, 43.70450415, 27.18992309, 30.04546242, 33.10752382,
        45.21072735, 43.27943877, 38.03905714,  3.50779096, 40.63280415,
        24.6706847 , 42.19601531, 39.17667707, 30.45211719, 37.36879691,
        34.83425981, 48.999747  , 35.18179324, 40.63280415, 33.28592469,
        38.03905714, 44.07609587, 47.69617883, 45.56482811, 42.19601531,
        36.8969263 , 50.98699463, 41.94125154, 38.11249792, 45.21072735,
        37.66878634, 37.8175141 , 39.54094628, 35.00036151, 38.33162015]),
 0.3971088052992405)

2

tuple


sns.boxplot(x);


dff.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   City             420 non-null    object 
 1   Area             420 non-null    object 
 2   Owner            420 non-null    object 
 3   BHK              420 non-null    object 
 4   Floor            420 non-null    object 
 5   price_in_crores  420 non-null    int32  
 6   Carpet_Area      420 non-null    float64
 7   per_sqt_rupees   420 non-null    int32  
dtypes: float64(1), int32(2), object(5)
memory usage: 23.1+ KB


# finding the skweness of the column
dff.price_in_crores.skew()

2.7504799201818098


dff.price_in_crores=dff.price_in_crores.astype(int)


Q1, Q2, Q3 = tuple(dff.price_in_crores.quantile(q = [0.25,0.5,0.75]).values)


print(Q1, Q2, Q3)

IQR = Q3-Q1
UL = Q3 + 1.5*IQR
LL = Q1-1.5*IQR

print(IQR, UL, LL)

3775000.0 6000000.0 12000000.0
8225000.0 24337500.0 -8562500.0


dff[(dff.price_in_crores > UL) | (dff.price_in_crores < LL)]


# log transformation method
np.log(dff.price_in_crores)

0     15.61
1     15.89
2     15.38
3     16.42
4     15.62
       ... 
415   15.20
416   15.75
417   15.61
418   15.61
419   15.79
Name: price_in_crores, Length: 420, dtype: float64


sns.boxplot(np.log(dff.price_in_crores));


# cube root transformation method
dff.price_in_crores**(1/3)

0     181.71
1     200.00
2     168.69
3     238.11
4     182.72
       ...  
415   158.74
416   190.38
417   181.71
418   181.71
419   193.10
Name: price_in_crores, Length: 420, dtype: float64


sns.boxplot(dff.price_in_crores**(1/3));


# Box-Cox tranformation method
stats.boxcox(dff.price_in_crores)

len(stats.boxcox(dff.price_in_crores))

type(stats.boxcox(dff.price_in_crores))

x,y = stats.boxcox(dff.price_in_crores)

(array([27.95079616, 28.79848153, 27.30470585, 30.38383809, 27.99904977,
        30.26757691, 27.42216391, 31.28002149, 17.53617617, 29.52749432,
        30.60479773, 33.73538618, 28.40293621, 28.9792627 , 29.40661236,
        27.95079616, 28.72320672, 27.80140474, 29.70078348, 27.80140474,
        26.40692943, 31.28002149, 32.63292756, 29.8113948 , 17.53617617,
        30.60479773, 28.79848153, 30.38383809, 31.76755768, 34.68958496,
        28.52685189, 25.12201448, 31.98743902, 32.57353486, 26.85423153,
        35.22289001, 30.02199489, 30.26757691, 28.18497291, 33.64823654,
        32.57353486, 29.52749432, 29.31289647, 27.95079616, 24.09250186,
        26.15573972, 29.40661236, 34.5344799 , 34.24778452, 28.40293621,
        29.61529192, 25.97578756, 30.14707386, 31.45043446, 30.81199497,
        30.21990256, 19.10014485, 32.35088585, 17.53617617, 30.91096563,
        27.6976861 , 28.79848153, 27.95079616, 27.95079616, 28.9792627 ,
        27.36399308, 27.6976861 , 27.59039329, 28.36052991, 30.14707386,
        29.04881391, 26.15573972, 28.44478198, 28.9792627 , 27.24425686,
        27.18259786, 26.98983043, 29.89196429, 25.6837186 , 32.88285199,
        27.90178761, 27.11967755, 26.40692943, 28.56710454, 26.71232601,
        27.18259786, 30.26757691, 26.78410492, 28.40293621, 35.99229238,
        34.43670673, 28.48608236, 29.37567425, 28.09338592, 26.40692943,
        35.42808859, 27.24425686, 17.53617617, 26.63881196, 25.97578756,
        36.91547391, 25.97578756, 25.97578756, 27.6976861 , 27.95079616,
        28.52685189, 27.11967755, 29.11689157, 28.40293621, 27.36399308,
        34.14946907, 27.74997671, 29.37567425, 27.24425686, 28.72320672,
        33.76965688, 25.57961147, 26.92278282, 26.63881196, 28.72320672,
        28.04657215, 36.06113177, 28.68489264, 33.07482362, 35.68838963,
        28.60685371, 26.78410492, 27.85199919, 27.95079616, 27.74997671,
        27.47926106, 28.68489264, 26.06711555, 26.24182098, 29.89196429,
        28.31754728, 28.87202356, 26.15573972, 27.85199919, 27.11967755,
        27.11967755, 27.11967755, 30.14707386, 26.78410492, 27.30470585,
        29.61529192, 27.95079616, 29.18355899, 27.30470585, 30.14707386,
        26.85423153, 28.68489264, 32.12656242, 27.85199919, 30.56176608,
        31.45043446, 26.63881196, 26.15573972, 34.64307786, 27.24425686,
        25.78429452, 27.6976861 , 26.78410492, 26.40692943, 27.30470585,
        27.85199919, 31.00709651, 25.47170593, 24.86327953, 24.26409673,
        28.27397184, 25.47170593, 25.78429452, 26.56347349, 26.78410492,
        28.22978638, 23.9099021 , 26.32550611, 28.18497291, 25.12201448,
        27.95079616, 28.79848153, 25.12201448, 26.06711555, 25.47170593,
        25.24326576, 30.81199497, 27.11967755, 28.761066  , 27.11967755,
        25.97578756, 28.60685371, 27.11967755, 26.40692943, 28.18497291,
        27.24425686, 31.53253095, 25.47170593, 26.06711555, 26.40692943,
        25.47170593, 29.15039777, 27.42216391, 28.87202356, 24.72473424,
        27.95079616, 22.46030498, 25.6837186 , 26.92278282, 30.02199489,
        34.43670673, 27.18259786, 26.56347349, 25.35970284, 27.30470585,
        35.52392467, 17.53617617, 25.6837186 , 24.09250186, 26.40692943,
        28.18497291, 31.76755768, 33.23707247, 29.40661236, 34.14179368,
        34.50681354, 28.79848153, 35.29629949, 27.74997671, 34.64307786,
        28.09338592, 32.38900235, 35.7034398 , 25.24326576, 27.6976861 ,
        27.0554412 , 27.47926106, 28.18497291, 26.24182098, 27.42216391,
        28.79848153, 31.28002149, 30.71000484, 27.11967755, 24.42597887,
        29.24887544, 26.40692943, 36.26895868, 34.32892521, 32.45161133,
        30.02199489, 27.64450237, 30.49615582, 31.36630055, 29.43725783,
        28.9792627 , 26.92278282, 17.53617617, 33.42051256, 29.24887544,
        30.02199489, 30.31457295, 30.91096563, 32.12656242, 25.78429452,
        37.31086014, 27.18259786, 30.71000484, 32.02977097, 21.28510544,
        25.12201448, 28.761066  , 35.73335791, 27.85199919, 34.91979099,
        28.18497291, 19.10014485, 29.49769337, 28.18497291, 26.40692943,
        24.72473424, 26.78410492, 31.28002149, 28.18497291, 28.60685371,
        34.50681354, 29.15039777, 28.761066  , 28.18497291, 27.42216391,
        27.95079616, 26.40692943, 26.15573972, 28.60685371, 29.49769337,
        26.78410492, 30.02199489, 27.11967755, 35.68335928, 27.30470585,
        25.47170593, 28.68489264, 28.31754728, 27.53532484, 32.81644729,
        33.10582129, 33.17710299, 25.97578756, 26.92278282, 28.60685371,
        25.24326576, 33.07482362, 23.9099021 , 26.40692943, 25.47170593,
        26.24182098, 25.97578756, 33.25683744, 30.38383809, 27.36399308,
        25.97578756, 32.81644729, 26.24182098, 34.13410184, 25.47170593,
        26.48621412, 34.09539327, 24.86327953, 30.14707386, 25.47170593,
        32.38900235, 34.14179368, 28.31754728, 26.40692943, 30.38383809,
        28.18497291, 34.21028857, 33.27649216, 26.92278282, 27.30470585,
        27.24425686, 33.90353605, 25.78429452, 26.40692943, 31.19148065,
        27.95079616, 36.30321153, 27.6976861 , 26.78410492, 26.92278282,
        27.0554412 , 28.18497291, 35.22289001, 27.47926106, 25.35970284,
        27.0554412 , 28.18497291, 25.35970284, 33.96863845, 29.15039777,
        26.63881196, 23.5050292 , 25.24326576, 23.71472489, 26.48621412,
        24.57922331, 25.24326576, 30.38383809, 23.27837951, 31.06348072,
        26.56347349, 25.78429452, 24.09250186, 31.41703123, 25.78429452,
        31.26249816, 24.72473424, 25.6837186 , 33.17710299, 30.60479773,
        24.99551698, 25.97578756, 25.12201448, 24.86327953, 24.86327953,
        27.90178761, 27.42216391, 23.03166067, 24.57922331, 25.97578756,
        28.79848153, 27.95079616, 27.11967755, 28.31754728, 29.31289647,
        27.85199919, 28.87202356, 27.85199919, 27.42216391, 28.9792627 ,
        27.85199919, 29.49769337, 27.95079616, 27.74997671, 27.30470585,
        35.53965802, 27.95079616, 28.60685371, 28.60685371, 28.40293621,
        27.53532484, 30.60479773, 28.9792627 , 30.96897411, 28.79848153,
        26.78410492, 28.36052991, 27.95079616, 27.95079616, 28.48608236]),
 0.06860705540578052)

2

tuple


sns.boxplot(x);


# Standardization

ss2 = StandardScaler()

dff4 = ss2.fit_transform(dff.price_in_crores.values.reshape(-1,1))

round(dff4.mean())
dff4.std()

0

1.0


sns.boxplot(dff4);


# Normalization
mm2 = MinMaxScaler()

dff5 = mm2.fit_transform(dff.price_in_crores.values.reshape(-1,1))

dff5.min()
dff5.max()


sns.boxplot(dff5);


dff.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   City             420 non-null    object 
 1   Area             420 non-null    object 
 2   Owner            420 non-null    object 
 3   BHK              420 non-null    object 
 4   Floor            420 non-null    object 
 5   price_in_crores  420 non-null    int32  
 6   Carpet_Area      420 non-null    float64
 7   per_sqt_rupees   420 non-null    int32  
dtypes: float64(1), int32(2), object(5)
memory usage: 23.1+ KB


dff.isna().sum()

City               0
Area               0
Owner              0
BHK                0
Floor              0
price_in_crores    0
Carpet_Area        0
per_sqt_rupees     0
dtype: int64


dff.to_csv('MagicBricks_cleaned.csv')


# import libraries
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style('whitegrid')
plt.style.use('bmh')

import warnings
warnings.filterwarnings('ignore')

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'

# for HD visualizations
%config InlineBackend.figure_format='retina'


data = pd.read_csv(r"C:\Users\GUDLA RAGUWING\Data Science Course\MagicBrics_Webscraping_project\MagicBricks_cleaned.csv")


data


# Droping unnecessery columns
data.drop(['Unnamed: 0'], axis=1, inplace=True)


# mathematical computation
data.price_in_crores=data.price_in_crores.values/10000000


data.head(15)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   City             420 non-null    object 
 1   Area             420 non-null    object 
 2   Owner            420 non-null    object 
 3   BHK              420 non-null    int64  
 4   Floor            420 non-null    object 
 5   price_in_crores  420 non-null    float64
 6   Carpet_Area      420 non-null    float64
 7   per_sqt_rupees   420 non-null    int64  
dtypes: float64(2), int64(2), object(4)
memory usage: 26.4+ KB


data.describe()


%config InlineBackend.figure_format='retina'

# magic command to generate HD charts


plt.figure(figsize=(7,7), dpi = 300)
explode = (0.1,0.1,0.1,0.1,0.1)
plt.pie(x = data.Area.value_counts().values[0:5],
        labels = data.Area.value_counts().index[0:5], autopct = '%.2f%%', radius = 0.7, pctdistance=0.5,explode =explode)
plt.title('Percentage of Properties available in top Five Areas')
plt.legend(loc = 'upper right')
plt.show();


plt.figure(figsize=(15,5), dpi=300)
plt.title('Number_of properties for the range of Carpet_Area', fontsize=20, color = 'Black')
plt.xlabel('Carpet_Area',fontsize = 20, color = 'black')
plt.ylabel('count',fontsize = 20, color = 'black')
sns.histplot( x ='Carpet_Area', data = data, color = 'orange');


plt.figure(figsize=(12,5), dpi = 300)
plt.boxplot(data.BHK,patch_artist=True, medianprops=dict(color = 'black'))
plt.title('Distribution of Properties over BHKs', color = 'red',fontsize=20)
plt.xlabel('BHK',fontsize = 20, color = 'black')
plt.ylabel('count',fontsize = 20, color = 'black')
plt.show();


data.BHK = data.BHK.astype(str)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 420 entries, 0 to 419
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   City             420 non-null    object 
 1   Area             420 non-null    object 
 2   Owner            420 non-null    object 
 3   BHK              420 non-null    object 
 4   Floor            420 non-null    object 
 5   price_in_crores  420 non-null    float64
 6   Carpet_Area      420 non-null    float64
 7   per_sqt_rupees   420 non-null    int64  
dtypes: float64(2), int64(1), object(5)
memory usage: 26.4+ KB


sns.__version__

'0.12.1'


plt.figure(figsize=(12,5), dpi = 300)
plt.title(' Count of BHKs', color = 'Green',fontsize=20)
sns.countplot(x=data.BHK, width = 0.5);


plt.figure(figsize=(12,5), dpi = 300)
plt.title(' Price_in_Crores Vs City', color = 'blue',fontsize=20)
sns.barplot(x=data.price_in_crores, y= data.City, ci= False, estimator='median' );


plt.figure(figsize=(12,5), dpi = 300)
plt.title('Top 12 Areas Vs Price_in_Crores', color = 'blue',fontsize=20)
plt.xlabel('Areas',fontsize = 15, color = 'black')
plt.ylabel('Price_in_Crores',fontsize = 15, color = 'black')
plt.xticks(rotation = 90)
sns.barplot(x=data.Area.value_counts().index[0:12], y= data.price_in_crores.value_counts().index[0:12], ci= False, width = 0.5);


plt.figure(figsize=(12,5), dpi = 300)
plt.scatter(data.Carpet_Area, data.per_sqt_rupees, label = 'per_sqt_rupees')

plt.title('Scatter plot - Carpet_Area vs per_sqt_rupees', fontsize=20)

plt.xlabel('Carpet_Area',fontsize = 15, color = 'black')
plt.ylabel('per_sqt_rupees',fontsize = 15, color = 'black')
plt.legend()
plt.show();


plt.figure(figsize=(12,5), dpi = 300)
plt.scatter(data.Carpet_Area, data.price_in_crores, label = 'price')

plt.title('Scatter plot - Carpet_Area vs price_in_crores', fontsize=20)
plt.xlabel('Carpet_Area',fontsize = 15, color = 'black')
plt.ylabel('price_in_crores',fontsize = 15, color = 'black')
plt.legend()
plt.show();


# plot corsstab data

pd.crosstab(index=data.City, columns=data.BHK)


i=pd.crosstab(index=data.City, columns=data.BHK)


plt.figure(figsize=(15,7), dpi=300)
plt.title('HeatMap - BHK vs City', fontsize=20)
sns.heatmap(i, annot=True, fmt = '.2f', linewidths=0.5, cmap='cool', linecolor='black');
plt.xlabel('BHK',fontsize = 15, color = 'black')
plt.ylabel('City',fontsize = 15, color = 'black');
# fmt=d for integers; fmt = .2f for float with 2 decimals


data.BHK=data.BHK.astype(int)


data_cor = data.corr()


plt.figure(figsize=(16,10), dpi=300)
plt.title('Correlation between Numerical columns', fontsize=20)
sns.heatmap(data_cor, annot=True, fmt = '.2f', linewidths=0.5, cmap='cool', linecolor='black');

# fmt=d for integers; fmt = .2f for float with 2 decimals


%%time
# the above command is a magic command to display wall time in seconds
plt.figure(figsize=(16,8), dpi= 300)
sns.pairplot(data)
plt.show();

<Figure size 4800x2400 with 0 Axes>

CPU times: total: 4.8 s
Wall time: 5.69 s


plt.figure(figsize=(15,5), dpi = 300)
sns.relplot(data = data, x = 'price_in_crores', y= 'City',hue = 'BHK')
plt.show();

<Figure size 4500x1500 with 0 Axes>


plt.figure(figsize=(15,5), dpi = 300)
sns.catplot(x="price_in_crores", y="City", kind="swarm",hue = 'BHK',color = 'red', data=data);

<Figure size 4500x1500 with 0 Axes>


plt.figure(figsize=(15,8), dpi = 300)
sns.catplot(x="price_in_crores",y="City",kind='violin',data=data);

<Figure size 4500x2400 with 0 Axes>


data.tail()


def user_data_frame():
    Data = input('enter price_in_croers or BHK or City')
    if Data=='City':
        x = input('enter the City name')
        if(x=='bangalore'):
            return(data[data.City=='bangalore'])
        elif(x=='mysore'):
            return(data[data.City=='mysore'])
    elif Data=='BHK':
        x = int(input('enter the BHK Number'))
        if(x==2):
            return(data[data.BHK==2])
        elif(x==3):
            return(data[data.BHK==3])
    elif Data =='price_in_crores':
        x = float(input('enter the price'))
        if(x>0.9):
            return(data[data.price_in_crores>0.9])


user_data_frame()

enter price_in_croers or BHK or CityBHK
enter the BHK Number2

	BHK	price_in_crores	Carpet_Area	per_sqt_rupees
count	420.000000	420.000000	420.000000	420.000000
mean	2.378571	1.221786	1156.371429	5144.238095
std	0.754947	1.634996	530.553297	3553.470033
min	1.000000	0.010000	9.000000	1500.000000
25%	2.000000	0.377500	812.000000	3295.000000
50%	2.000000	0.600000	1052.500000	4274.000000
75%	3.000000	1.200000	1400.000000	5481.250000
max	5.000000	10.900000	3900.000000	27136.000000

	City	Area	Owner	BHK	Floor	price	Carpet_Area	per_sqt
0	bangalore	Kattigenahalli	Omkar Pandey	3	FloorGround out of 4	₹60 Lac	1000	₹4950
1	bangalore	Ayodaya Nagar	gokul	2	FloorGround out of 4	₹80 Lac	1150	₹6957
2	bangalore	Electronic City	Thayumanavan	2	Floor4 out of 4	₹48 Lac	1060	₹4528
3	bangalore	Tumkur Road	Prasanna	3	Floor14 out of 15	₹1.35 Cr	1790	₹7542
4	bangalore	Kudlu Gate	Akash Akash	2	Floor2 out of 4	₹61 Lac	803	₹5706
5	bangalore	Hal Stage 2	av nath	3	Floor1 out of 4	₹1.30 Cr	1640	₹7303
6	bangalore	Murugeshpalya	Sangeeta Pillai	2	Floor3 out of 5	₹50 Lac	1250	₹5000
7	bangalore	Begur Road	ganga k	3	Basement out of 17	₹1.80 Cr	1588	₹11335
8	bangalore	Judicial Layout	Anjan K	3	Floor3 out of 14	₹1 Cr	1000	₹8889
9	bangalore	Bellandur	Omesh Saraf	2	Floor2 out of 10	₹1.02 Cr	1465	₹6997
10	bangalore	Kasturi Nagar	Mohd Hussain	3	FloorGround out of 7	₹1.45 Cr	1500	₹8146
11	bangalore	Koramangala Block 1	Rahul Jain	3	Floor16 out of 16	₹3.85 Cr	1418	₹20632
12	bangalore	Sarjapur Road	Gaurav Kumar GAURAV	3	FloorGround out of 5	₹70 Lac	1350	₹5655
13	bangalore	Devanahalli	Ancy	2	Floor1 out of 7	₹85 Lac	1000	₹6071
14	bangalore	Hanumantha Nagar	Anoop	2	Floor2 out of 4	₹98 Lac	1250	₹9800
15	bangalore		geetha	2	Floor2 out of 5	₹60 Lac	857	₹6367
16	bangalore	Doddakannelli	Omkar Omkar	2	Floor3 out of 6	₹78 Lac	753	₹5158
17	bangalore	Electronic City	ruchika	2	Floor1 out of 7	₹57 Lac	1186	₹6374
18	bangalore	Sarjapur Road	jeswanth	3	Floor1 out of 4	₹1.08 Cr	1250	₹4553
19	bangalore	Jigani	vikas saxena	3	Floor6 out of 12	₹57 Lac	770	₹4545

	City	Area	Owner	BHK	Floor	price	Carpet_Area	per_sqt
0	ranchi	H	Waseem Ahmad	3	Floor3 out of 4	₹62 Lac	1110	₹4655
1	ranchi		Anuj Krchandra	3	Floor5 out of 6	₹76.4 Lac	1500	₹4200
2	ranchi	S	Rabindra Bakshi	3	Floor4 out of 4	₹77 Lac	1150	₹5325
3	ranchi	Ratu Road	Abhishek Gupta	1	Floor2 out of 4	₹31.5 Lac	573	₹5497
4	ranchi	Harihar S	Shree Ram Developers	3	Floor8 out of 12	₹68.6 Lac	1560	₹4400
5	ranchi	Bariatu Road	vivek chandra	3	Floor2 out of 4	₹75 Lac	2010	₹3731
6	ranchi	Morabadi	sushant Bhattacharjee	2	Floor1 out of 4	₹40 Lac	930	₹3774
7	ranchi	Bahu Bazar	Ritesh Nagpal	3	Floor2 out of 7	₹58 Lac	1280	₹4531
8	ranchi		Nitin Kumar	3	Floor4 out of 4	₹60 Lac	1250	₹4317
9	ranchi	Vikas Nagar	PARTHO BOSE	3	Floor1 out of 1	₹56 Lac	1500	₹3733
10	ranchi	Bariatu	Pradeep	3	Floor3 out of 4	₹51 Lac	1385	₹3682
11	ranchi	Hesag	rajesh singh	3	Floor4 out of 6	₹77 Lac	1212	NaN
12	ranchi		ashish singh	2	Floor1 out of 4	₹31 Lac	1000	₹2897
13	ranchi	Barga	sanjeev	2	Floor3 out of 3	₹33 Lac	800	₹3986
14	ranchi	Sector 2 Masibari	Kshav	3	Floor9 out of 10	₹1.15 Cr	1925	₹5476
15	ranchi	Upper Chutia	Abhishek	3	Floor4 out of 4	₹68 Lac	1306	₹5207
16	ranchi	Hazaribag Road	Neeraj	3	Floor4 out of 5	₹82 Lac	1540	₹5325
17	ranchi	Namkum	Manit Kumar	2	Floor3 out of 4	₹32 Lac	1180	₹2712
18	ranchi	Lalpur	Mukesh Mukesh	2	FloorGround out of 6	₹58 Lac	971	₹4629
19	ranchi	Hesag	Samita Chatterjee	2	Floor3 out of 4	₹45 Lac	950	₹4110

	City	Area	Owner	BHK	Floor	price	Carpet_Area	per_sqt
0	greater-noida	Zeta 1	RAHUL TRIVEDI	2	Floor17 out of 22	₹47.5 Lac	1200	₹3958
1	greater-noida	Sector P4	R Bhattacharjee	3	Floor7 out of 9	₹46 Lac	1120	₹3286
2	greater-noida	Eta 2	Ashutosh	3	Floor8 out of 25	₹37 Lac	995	NaN
3	greater-noida	Sector 3	Sandeep gautam	2	Floor1 out of 2	₹24 Lac	970	₹3719
4	greater-noida	Zeta 1	vijay	2	Floor8 out of 16	₹48 Lac	895	₹2474
...	...	...	...	...	...	...	...	...
205	mysore		Nayana A.S	2	FloorGround out of 3	₹40 Lac	1070	₹6389
206	mysore	Kuvempunagar	Srikanth	2	Floor1 out of 3	₹69 Lac	1080	₹5000
207	mysore		MANJUNATHA	2	FloorGround out of 2	₹60 Lac	1200	NaN
208	mysore	V V Mohalla	Chakko	2	FloorGround out of 5	₹60 Lac	900	₹6667
209	mysore	Sriramapura	NaN	2	Floor1 out of 3	₹72 Lac	1115	₹6457

	City	Area	Owner	BHK	Floor	price	Carpet_Area	per_sqt
0	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...
415	False	False	False	False	False	False	False	False
416	False	False	False	False	False	False	False	False
417	False	False	False	False	False	False	False	True
418	False	False	False	False	False	False	False	False
419	False	False	True	False	False	False	False	False

	City	Area	Owner	BHK	Floor	price	Carpet_Area	per_sqt
32	mumbai	Borivali West	Sachin Kunder	2	Floor5 out of 15	₹2.25 Cr	1160.00	NaN
39	mumbai	Mahim West	Satyajit Satyajit	2	Floor6 out of 12	₹3.75 Cr	800.00	NaN
43	mumbai		Haroon mansuri	2	Floor21 out of 21	₹60 Lac	1870.00	NaN
60	chennai	Mannivakkam Chennai	Yogesh Baskaran	2	FloorGround out of 2	₹55 Lac	800.00	NaN
67	chennai	Mogappair Chennai	Elango Rajendran	2	Floor16 out of 16	₹53 Lac	852.00	NaN
115	visakhapatnam	Tagarapuvalsa	Vision Properties	3	Floor3 out of 5	₹38.9 Lac	800.00	NaN
116	visakhapatnam	Parawada	Honey Group	2	Floor5 out of 5	₹26 Lac	900.00	NaN
117	visakhapatnam	PM Palem	New Living Properties	2	Floor3 out of 5	₹42 Lac	650.00	NaN
118	visakhapatnam	Madhurwada	podilapu simhachalam	2	NaN	₹38 Lac	800.00	NaN
119	visakhapatnam	MVP Colony	Dinakar	2	NaN	₹78 Lac	NaN	NaN
131	ranchi	Hesag	rajesh singh	3	Floor4 out of 6	₹77 Lac	1212.00	NaN
145	ranchi		taha	5	FloorGround out of 8	₹1.05 Cr	1650.00	NaN
173	haridwar	Jwalapur	Abhishek chaudhary	2	Floor3 out of 3	₹37 Lac	900.00	NaN
191	vadodara	Nani Bapod	Umakant parmar	1	Floor1 out of 3	₹7.5 Lac	1010.00	NaN
192	vadodara	Race Course circle	Akshat Dani	2	Floor3 out of 5	₹45 Lac	850.00	NaN
199	vadodara	Bhayli	Shashank	2	Floor4 out of 4	₹35 Lac	1150.00	NaN
204	vadodara	Sayajipura	Ankur Javia	2	Floor5 out of 5	₹19 Lac	1580.00	NaN
212	greater-noida	Eta 2	Ashutosh	3	Floor8 out of 25	₹37 Lac	995.00	NaN
216	greater-noida	Greater Noida West	Kamal Bansal	4	Floor18 out of 32	₹1 Cr	2364.00	NaN
242	gurgaon	Kendriya Vihar	Arun Yadav	3	Floor10 out of 14	₹1.50 Cr	1500.00	NaN
269	gurgaon	Sector 65	Manjunath S	3	FloorGround out of 32	₹5 Cr	3069.00	NaN
275	mangalore	Kulai	Devdhar Shetty	2	Floor4 out of 5	₹65 Lac	906.00	NaN
277	mangalore	Ujire	Stany		Floor3 out of 4	₹10.1 Lac	NaN	NaN
281	mangalore	Shakti Nagar	Iqbal	2	Floor5 out of 5	₹40 Lac	1166.00	NaN
298	mangalore	Nandigudda	SNEHA	3	Floor3 out of 5	₹68.5 Lac	1341.00	NaN
394	mysore	Yadavgiri	Jagadeesha	2	Floor2 out of 4	₹95 Lac	1280.00	NaN
406	mysore	Vidyarayanapuram	Narasimha murthy	3	Floor1 out of 4	₹60 Lac	1553.00	NaN
407	mysore	Srirangapatnam	Vijay Menon	3	Floor3 out of 4	₹75 Lac	1875.00	NaN
417	mysore		MANJUNATHA	2	FloorGround out of 2	₹60 Lac	1200.00	NaN

	BHK	price	Carpet_Area	per_sqt
40	2	₹2.70 Cr	450.00	₹11529
41	1	₹1.02 Cr	430.00	₹38459
42	1	₹95 Lac	675.00	₹7242
43	2	₹60 Lac	1870.00	NaN
44	4	₹15 Cr	820.00	₹10000
45	2	₹32 Lac	850.00	₹14583
46	2	₹98 Lac	1172.00	₹5376
47	3	₹4.89 Cr	404.00	₹12042
48	1	₹44.9 Lac	575.00	₹13669
49	2	₹70 Lac	507.00	₹19375

	City	Area	Owner	BHK	Floor	price_in_crores	Carpet_Area	per_sqt_rupees
154	haridwar	Laksar Road	Shakti Verma	1	1 out of 4	14300000	3000.00	2192
200	vadodara	Danteshwar	Pushpita Roy Choudhury	2	4 out of 9	2500000	2358.00	2174
202	vadodara	Gotri Sevasi Road	Deepak Singh	3	9 out of 9	5000000	2350.00	5556
208	vadodara	Alwar Bypass Road	Bindi shah	3	2 out of 2	4200000	2600.00	2545
216	greater-noida	Greater Noida West	Kamal Bansal	4	18 out of 32	100000	2364.00	4274
221	greater-noida	Chi 5	AN SINHA	4	11 out of 22	21000000	3210.00	6533
222	greater-noida	Jaypee Greens	Nihit	4	6 out of 34	33100000	3441.00	6542
248	gurgaon	Sector 112	Y S Dwivedi	3	9 out of 15	46000000	3763.00	12224
258	gurgaon	Sector 47	Pradeep Sharma	3	1 out of 4	35000000	2470.00	14170
265	gurgaon	Sector 54	VIVEK SHARMA	3	City Plot Phase 1	109000000	3650.00	25952
269	gurgaon	Sector 65	Manjunath S	3	Ground out of 32	500000	3069.00	4274
276	mangalore	Bajpe	Gopal	4	4 out of 8	200000	3030.00	5882
282	mangalore	Falnir	Santosh Babu Salian	3	2 out of 4	18000000	3900.00	4615
346	agra	Sikandra	SHWETANG SHARMA	5	3 out of 5	81900000	2410.00	3997

	City	Area	Owner	BHK	Floor	price_in_crores	Carpet_Area	per_sqt_rupees
142	ranchi	Alwar Bypass Road	Avishek Modi Modi	4	3 out of 4	12500000	2250.00	5556
154	haridwar	Laksar Road	Shakti Verma	1	1 out of 4	14300000	3000.00	2192
200	vadodara	Danteshwar	Pushpita Roy Choudhury	2	4 out of 9	2500000	2358.00	2174
202	vadodara	Gotri Sevasi Road	Deepak Singh	3	9 out of 9	5000000	2350.00	5556
208	vadodara	Alwar Bypass Road	Bindi shah	3	2 out of 2	4200000	2600.00	2545
216	greater-noida	Greater Noida West	Kamal Bansal	4	18 out of 32	100000	2364.00	4274
221	greater-noida	Chi 5	AN SINHA	4	11 out of 22	21000000	3210.00	6533
222	greater-noida	Jaypee Greens	Nihit	4	6 out of 34	33100000	3441.00	6542
248	gurgaon	Sector 112	Y S Dwivedi	3	9 out of 15	46000000	3763.00	12224
258	gurgaon	Sector 47	Pradeep Sharma	3	1 out of 4	35000000	2470.00	14170
265	gurgaon	Sector 54	VIVEK SHARMA	3	City Plot Phase 1	109000000	3650.00	25952
269	gurgaon	Sector 65	Manjunath S	3	Ground out of 32	500000	3069.00	4274
276	mangalore	Bajpe	Gopal	4	4 out of 8	200000	3030.00	5882
282	mangalore	Falnir	Santosh Babu Salian	3	2 out of 4	18000000	3900.00	4615
346	agra	Sikandra	SHWETANG SHARMA	5	3 out of 5	81900000	2410.00	3997

BHK	1	2	3	4	5
City
agra	0	16	12	0	2
bangalore	0	12	17	1	0
bhiwadi	5	22	3	0	0
chennai	4	22	4	0	0
greater-noida	1	15	10	4	0
gurgaon	1	7	16	6	0
haridwar	8	13	8	1	0
mangalore	1	18	10	1	0
mumbai	11	14	4	1	0
mysore	1	17	9	3	0
raipur	2	15	13	0	0
ranchi	1	9	18	1	1
vadodara	4	14	9	3	0
visakhapatnam	1	14	15	0	0

Check for duplicated()¶

No duplicates found in my DataFrame¶

Handling Missing Values¶

Identify Missing Values¶

View the missing values¶

Identify the corrupted data¶

some columns should be of int and float but not , so we need to investigate why it is happened¶

replacing Symbols and text with nothing¶

Filling the numerical missing values with mean or median of the column whichever is suitable based-on a serious analysis¶

BHK Column¶

Area Column¶

Owner Column --- Catagorical¶

Floor Column --- Catagorical¶

per_sqt column --- Numerical¶

Check the skewness of the per_sqt column¶

Replacing Some of the values which are wrong with NaN's and filling them with median¶

Identifying Outliers¶

IQR Method¶

Z-score method¶

Applying Transformation Techniques¶

Carpet_Area Column¶

Identifying Outliers¶

IQR Method¶

Z-score Method¶

Applying Transformation Techniques¶

price_in_crores column --- numerical¶

Identifying Outliers¶

IQR Method¶

Applying Transformation Techniques¶

Univariate Analysis¶

Catagorical¶

Pie Chart¶

Numerical¶

Histogram¶

Box Plot¶

Bivariate Analysis¶

1.Categorical Vs Catagorical¶

Count Plot¶

Categorical & Numerical¶

Bar Chart¶

Bar-Chart¶

Numerical Vs Numerical¶

Scatter-plot¶

Scatter plot¶

Heat-Map Using Crosstab¶

Heat Map using Corellation¶

Multi-Variate¶

Pair-plot¶

Multi-Variate analysis Using hue parameter¶

Relational Plot's¶

catplot ----> Kind = 'swarm'¶

catplot ----> Kind = 'violin '¶

Creating User defined function to view the data frame accordingly¶

Multi-Variate analysis Using `hue` parameter¶